Search

The roll centre (RC) is a core parameter for vehicle lateral dynamics and control, which can be obtained via suspension geometry configuration or suspension kinematics and compliance (K&C) test. However, these methodologies are used for laboratory tests and are suitable at low lateral acceleration. In other words, the RC is hard to measure directly while the vehicle is running on the road. In this paper, the online RC estimation methodologies including the adaptive sliding mode observer (ASMO) and the extended Kalman filter (EKF) only with roll rate are proposed considering vehicle transient dynamics. The performance of these algorithms is evaluated and compared with the recursive least square with disturbance observer algorithm (RLSDA) via vehicle dynamics study. Simulation results manifest that, compared with the RLSDA with three roll signals, the proposed ASMO and EKF, only with roll rate, can estimate RC successfully for both the transient and steady-state cases and can be applied for online vehicle RC estimation. In detail, the proposed ASMO is recommended for the steady-state case, and the proposed EKF is recommended for the transient case. Furthermore, the static RC is recommended as the estimation initial value to improve estimation. [ABSTRACT FROM AUTHOR]

This paper presents a novel observer-based control strategy to improve the vehicle roll behavior performance through magneto-rheological (MR) dampers under steering wheel input and various road excitation conditions. Since the vehicle roll with sudden steering input is an essential part of driving safety and possesses inherent nonlinearities, the full-car nonlinear Takagi-Sugeno (T-S) fuzzy model is first established to describe the vehicle roll dynamics considering nonlinear coupling dynamics of tire lateral force and MR damper force under road excitation input. Furthermore, a T-S model-based fuzzy observer is adapted to estimate the vehicle roll angle and roll rate. The stability conditions for the used T-S observer are calculated using linear matrix inequalities (LMIs), and the proposed observer is induced by solving the proposed LMI. Based on the Lyapunov function, sliding mode theory and prescribed performance function, a novel state observer-based prescribed performance control strategy is developed to constrain the controlled vehicle roll angle and roll rate state within the prescribed performance boundaries. Finally, the proposed techniques are validated through the J-turn and Fishhook tests conducted via a high-fidelity CarSim software platform.

The autodriver algorithm was introduced as a path-following algorithm for autonomous vehicles, which uses road geometry data and planar vehicle dynamics. In this paper, the autodriver algorithm is improved according to practical implications, and a more realistic vehicle model (roll mode) is used, which considers roll degree of freedom in addition to a planar motion. A Ghost-Car path-following approach is introduced to define the desired location of the car at every instance. Key steady-state characteristics of turning vehicles, namely the curvature, yaw rate, and side-slip responses, are discussed and used to construct a feed-forward component of a path-following controller based on the autodriver algorithm. Feedback control loops are designed and applied to minimise transient errors between the road and vehicle positions. Finally, simulations are performed to analyse the pathfollowing performance of the proposed scheme. The results show promising performance of the controller both in terms of error minimisation and passenger comfort. [ABSTRACT FROM AUTHOR]

Rollover index is an essential metric to detect vehicle rollover propensity, which is used to warn a driver or trigger an active rollover prevention system to perform corrective action. To give continuous and completed rollover information, this paper presents a novel rollover index based on a mass-center-position (MCP) metric. The MCP is first determined by estimating the positions of the center of mass of the vehicle, which consists of one sprung mass and two unsprung masses with two switchable roll motion models, before and after tire liftoff. The roll motion information can then be provided through the MCP continuously without saturation for both tripped and untripped rollovers. Moreover, to describe the completed rollover status, different criteria are derived from D'Alembert's principle and the moment balance conditions based on the MCP. In addition to tire liftoff, three new rollover statuses, “rollover threshold,” “rollover,” and vehicle jumping “in the air,” can be all identified by the proposed criteria. Furthermore, the sensitivity analysis is conducted on some key parameters, such as the sprung/unsprung masses and the height of the center of gravity, to validate the robustness of the proposed detection method. Compared with an existing representative rollover index, lateral load transfer ratio, the fishhook maneuver simulation results in CarSim for an electric vehicle show that the MCP metric can successfully predict vehicle impending rollover status continuously and completely for both untripped and tripped rollovers. [ABSTRACT FROM AUTHOR]

For vehicle rollover control systems, an accurate and predictive rollover index is necessary for a precise rollover threat detection and rollover prevention. In this paper, the contour line of load transfer ratio (CL-LTR) and the CL-LTR-based vehicle rollover index (CLRI) are proposed, describing LTR threshold and LTR change rate precisely, providing an accurate prediction of vehicle rollover threat. In detail, the CL-LTR is proposed via the roll dynamics phase plane analysis, and its analytical solution of one-degree-of-freedom vehicle roll model and extension for full vehicle are derived. Moreover, the predictive CLRI is proposed and evaluated via vehicle dynamics study. The results demonstrate that vehicle rollover threat is predicted accurately based on the CLRI, which shows benefits for the vehicle rollover prediction and stability control. [ABSTRACT FROM PUBLISHER]

Accurate lateral load transfer estimation plays an important role in improving the performance of the active rollover prevention system equipped in commercial vehicles. This estimation depends on the accurate prediction of roll angles for both the sprung and the unsprung subsystems. This paper proposes a novel computational method for roll-angle estimation in commercial vehicles employing sensors which are already used in a vehicle dynamic control system without additional expensive measurement units. The estimation strategy integrates two blocks. The first block contains a sliding-mode observer which is responsible for calculating the lateral tyre forces, while in the second block, the Kalman filter estimates the roll angles of the sprung mass and those of axles in the truck. The validation is conducted through MATLAB/TruckSim co-simulation. Based on the comparison between the estimated results and the simulation results from TruckSim, it can be concluded that the proposed estimation method has a promising tracking performance with low computational cost and high convergence speed. This approach enables a low-cost solution for the rollover prevention in commercial vehicles. [ABSTRACT FROM PUBLISHER]

This technical brief proposes a new model-based estimation method for the vehicle sideslip angle, yaw rate, roll angle, and roll rate using unscented Kalman filter (UKF). Since a vehicle wheel could potentially lift off the ground during the limit handling, a switched vehicle roll dynamics model (wheel lift and no wheel lift) is developed and integrated within the proposed model-based estimation approach considering the availability of wheel speed sensor. The simulation results and analyses demonstrate the performance enhancement of the proposed estimation method over the method not considering wheel lift during the limit handling. [ABSTRACT FROM AUTHOR]