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In order to improve handling stability performance and active safety of a ground vehicle, a large number of advanced vehicle dynamics control systems—such as the direct yaw control system and active front steering system, and in particular the advanced driver assistance systems—towards connected and automated driving vehicles have recently been developed and applied. However, the practical effects and potential performance of vehicle active safety dynamics control systems heavily depend on real-time knowledge of fundamental vehicle state information, which is difficult to measure directly in a standard car because of both technical and economic reasons. This paper presents a comprehensive technical survey of the development and recent research advances in vehicle system dynamic state estimation. Different aspects of estimation strategies and methodologies in recent literature are classified into two main categories—the model-based estimation approach and the data-driven-based estimation approach. Each category is further divided into several sub-categories from the perspectives of estimation-oriented vehicle models, estimations, sensor configurations, and involved estimation techniques. The principal features of the most popular methodologies are summarized, and the pros and cons of these methodologies are also highlighted and discussed. Finally, future research directions in this field are provided.

Accurate knowledge of vehicle inertial parameters (e.g. vehicle mass and yaw moment of inertia) is essential to manage vehicle potential trajectories and improve vehicle active safety. For lightweight electric vehicles (LEVs), whose control performance of dynamics system can be substantially affected due to the drastic reduction of vehicle weights and body size, such knowledge is even more critical. This study proposes a dual unscented Kalman filter (DUKF) approach, where two UKFs run in parallel to simultaneously estimate vehicle states and parameters such as vehicle velocity, vehicle sideslip angle, and inertial parameters. The proposed method only utilises real-time measurements from torque information of in-wheel motor and sensors in a standard car. The four-wheel non-linear vehicle dynamics model considering payload variations is developed, local observability of the DUKF observer is analysed and derived via differential geometry theory. To address the non-linearities in vehicle dynamics, the DUKF and dual extended Kalman filter (DEKF) are also presented and compared. Simulations with various manoeuvres are carried out using the platform of MATLAB/Simulink-Carsim ® . Simulation results of MATLAB/Simulink-Carsim ® show that the proposed DUKF method can effectively estimate inertial parameters of LEV under different payloads. Moreover, the investigation reveals that the proposed DUKF approach has better performance of estimating vehicle inertial parameters compared with the DEKF method.

A new cooperative braking control strategy (CBCS) is proposed for a parallel hybrid electric vehicle (HEV) with both a regenerative braking system and an antilock braking system (ABS) to achieve improved braking performance and energy regeneration. The braking system of the vehicle is based on a new method of HEV braking torque distribution that makes the antilock braking system work together with the regenerative braking system harmoniously. In the cooperative braking control strategy, a sliding mode controller (SMC) for ABS is designed to maintain the wheel slip within an optimal range by adjusting the hydraulic braking torque continuously; to reduce the chattering in SMC, a boundary-layer method with moderate tuning of a saturation function is also investigated; based on the wheel slip ratio, battery state of charge (SOC), and the motor speed, a fuzzy logic control strategy (FLC) is applied to adjust the regenerative braking torque dynamically. In order to evaluate the performance of the cooperative braking control strategy, the braking system model of a hybrid electric vehicle is built in MATLAB/SIMULINK. It is found from the simulation that the cooperative braking control strategy suggested in this paper provides satisfactory braking performance, passenger comfort, and high regenerative efficiency.

This paper presents a robust gain‐scheduled H∞ controller to improve lateral stability and handling of four‐wheel‐independent‐drive electric vehicles possessing active front steering system with considerations of state delay. The time delay for the state is assumed as uncertain time‐invariant but has a known constant bound. By considering the tyre cornering stiffness represented by the norm‐bounded uncertainty and the time‐varying longitudinal velocity described by a polytope with finite vertices, and the vehicle lateral dynamics model is converted into the uncertain vehicle linear parameter‐varying (LPV) state‐delayed system. The resulting delay‐dependent robust gain‐scheduling state feedback H∞ controller is finally designed utilizing the constructed Lyapunov‐Krakovskii functional, and solved via a set of delay‐dependent linear matrix inequalities (LMIs). Simulations for various driving scenarios are implemented using MATLAB/SIMULINK‐CARSIM. The simulation results show the effectiveness of the proposed controller.

A water modelling experiment was conducted to study the fluid flow in a continuous slab casting mould with solidified shell. The level fluctuation, residence time distribution and velocity of free surface have been varied in the water model to study the differences of flow behaviour between the mould with a shell and without a shell. The results show that the mould with a solidified shell has higher level fluctuations, higher surface velocities and worse liquid slag distribution. The tapering of the shell in the mould enabled downward flow to facilitate more fluid being 'pushed' into the upper recirculation zone, yielding higher velocities and greater turbulence at the top surface. With the consideration of the solidified shell, the fluid flow in the mould is more representative of real casters, and the physical modelling results will be more accurate and reliable. It may cause unrealistic lower surface level fluctuations and surface velocities in the water model when the solidified shell is neglected in the mould.