Aiming at the lack of adaptability of vehicle parameters under extreme conditions, this paper proposes an integrated control method for path tracking and lateral stability of distributed drive electric vehicles based on tire cornering stiffness adaptive model predictive control (MPC) scheme. The control method integrates active front steering and direct yaw control to improve the path tracking and lateral stability performance of distributed drive electric vehicles. Firstly, considering the influence of vertical load transfer, the tire cornering stiffness is estimated based on the extended Kalman filter (EKF) algorithm. Then, using this online updated tire cornering stiffness value, an adaptive MPC controller for path tracking and lateral motion stability of distributed drive electric vehicles is constructed. Meanwhile, a fuzzy sliding mode control (Fuzzy-SMC)–based longitudinal velocity controller is established to ensure the accuracy of velocity tracking. Also, according to the distributed driving characteristics of the controlled system, a tire torque distributor based on weighted pseudo-inverse (WPI) is designed with the minimum tire load rate as the optimization objective, where the road adhesion condition and the maximum output torque of the motor are considered as constraints. The simulation results show that the proposed integrated control method based on tire cornering stiffness adaptive model predictive control is robust and effective. Compared with constant cornering stiffness model predictive control–based control method, it can improve the vehicle path tracking accuracy and lateral motion stability under extreme conditions.

Background: Automobile anti-lock braking system (ABS) is an important part of automobile active safety control system, which is widely used in all kinds of automobiles. At present, the research of ABS mainly focuses on the research of control algorithm, which is intended to improve the stability, robustness and adaptability of the control algorithm. Objective: ABS control algorithm has become a research hotspot. Different research workers have proposed different control algorithms and patents because of the different tools used and the entry points of the research. These control algorithms have played a role in promoting the development of ABS. This article reviews various control algorithms. Method: According to the research status of domestic and foreign researchers in the field of ABS control algorithms, ABS control algorithms are mainly divided into two categories: control methods based on logic thresholds and control methods based on slip ratio. Results: The comparative study of ABS control methods shows that the logic threshold control method has strong maneuverability and simple implementation, but its adaptability is poor. Sliding mode control has strong robustness and good transient response, but chattering needs to be suppressed. Although the PID control algorithm is simple and easy to implement, it needs to improve the transient response of the system. In the future, it is necessary to explore adaptive robust control algorithms that adapt to extreme conditions such as high nonlinearity and road sudden changes, such as active disturbance rejection control technology, deep learning neural network control technology, etc.

Abstract. The measurement of the road adhesion coefficient is of great significance for the vehicle active safety control system and is one of the key technologies for future autonomous driving. With a focus on the problems of interference uncertainty and system nonlinearity in the estimation of the road adhesion coefficient, this work adopts a vehicle model with 7 degrees of freedom (7-DOF) and the Dugoff tire model and uses these models to estimate the road adhesion coefficient in real time based on the particle filter (PF) algorithm. The estimations using the PF algorithm are verified by selecting typical working conditions, and they are compared with estimations using the unscented Kalman filter (UKF) algorithm. Simulation results show that the road adhesion coefficient estimator error based on the UKF algorithm is less than 7 %, whereas the road adhesion coefficient estimator error based on the PF algorithm is less than 0.1 %. Thus, compared with the UKF algorithm, the PF algorithm has a higher accuracy and control effect with respect to estimating the road adhesion coefficient under different road conditions. In order to verify the robustness of the road adhesion coefficient estimator, an automobile test platform based on a four-wheel-hub-motor car is built. According to the experimental results, the estimator based on the PF algorithm can realize the road surface identification with an error of less than 1 %, which verifies the feasibility and effectiveness of the algorithm with respect to estimating the road adhesion coefficient and shows good robustness.

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According to the principle that the fibre-like arrangement of reinforcing SnO 2 particles paralleling to the direction of current is propitious to the electrical and mechanical performance of the electrical contact materials (ECM), we proposed and reported the novel precursor route used to prepare Ag/(SnO 2 ) 12 ECM with fibre-like arrangement of reinforcing nanoparticles. The as-prepared samples were characterized by means of X-ray diffraction (XRD), scanning electron microscopy (SEM), optical metallurgical (OM), energy-dispersive X-ray spectroscopy (EDX), MHV2000 microhardness test, and double bridge tester. The mechanism for the formation of fibre-like arrangement of reinforcing nanoparticles in Ag/(SnO 2 ) 12 ECM was also dicussed. The analysis results show that Ag/(SnO 2 ) 60 sphere with mosaic structure of SnO 2 nanoparticles embedded in Ag matrix is believed to play a pivotal role in formation of fibrelike structure. The fibre-like structured Ag/(SnO 2 ) 12 ECM exhibites a high elongation of 24%, a particularly low electrical resistivity of 2.08 , low arcing energy and low contact resistivity, and thus has considerable technical, economical and environmental benefits.