To improve the handling and maneuverability of four-wheel steer (4WS) and in-wheel motor driven electric vehicle (EV) in which the mechanical properties of tires are unknown, an optimal coordinated control combining active rear wheel steering (ARS) and direct yaw moment control (DYC) in the form of active drive torque distribution is proposed. By designing a new type of particle filter (PF), the online identification of unknown tire model is realized and its uncertainties and road surface disturbances are both considered. The constraints of vehicle motions tracking ideal values are derived according to the estimated tire lateral forces with Lyapunov stability theory. And then based on the thought of backstepping and optimization, the drive torque distribution method and the rear wheel steer strategy are obtained aiming at minimizing tire load and control inputs, which also take the effective working range of in-wheel motor into account. Finally, the validity and practicability of overall controller are verified through the simulation of Carsim and Matlab/Simulink. The proposed control strategy lays a theoretical foundation for the integration and optimization of 4WS EVs driven by in-wheel motors.

The tire/soil interaction is an important and complex research topic in vehicle-terrain mechanics, which has a great influence on vehicle mobility and soil compaction. The purpose of this work was to develop a detailed nonlinear finite element model for parametric analysis of the interaction between mechanical elastic wheel and the soil. The three-dimensional finite element model of the mechanical elastic wheel, which considers material nonlinearity, geometric nonlinearity, as well as large contact deformation between the mechanical elastic wheel and soil, was developed based on the physical model. The reliability and accuracy of the finite element model were validated by comparing the predicted wheel static loading characteristics in rigid plane with those of experiments. The finite element model of the soil is modeled as a two-layer system and the Extended Drucker–Prager model was used to describe the nature soil properties. The deformation and stress of the soil and mechanical elastic wheel under the static loading condition were studied in detail. Moreover, the effects of the axial load on mechanical elastic wheel/soil interaction were also analyzed. The research results could offer reference for an optimum structural design of a mechanical elastic wheel and a prediction of soil compaction caused by non-pneumatic tires.

Different driving styles should be considered in path planning for autonomous vehicles that are travelling alongside other traditional vehicles in the same traffic scene. Based on the drivers’ characteristics and artificial potential field (APF), an improved local path planning algorithm is proposed in this paper. A large amount of driver data are collected through tests and classified by the K-means algorithm. A Keras neural network model is trained by using the above data. APF is combined with driver characteristic identification. The distances between the vehicle and obstacle are normalized. The repulsive potential field functions are designed according to different driver characteristics and road boundaries. The designed local path planning method can adapt to different surrounding manual driving vehicles. The proposed human-like decision path planning method is compared with the traditional APF planning method. Simulation tests of an individual driver and various drivers with different characteristics in overtaking scenes are carried out. The simulation results show that the curves of human-like decision-making path planning method are more reasonable than those of the traditional APF path planning method; the proposed method can carry out more effective path planning for autonomous vehicles according to the different driving styles of surrounding manual vehicles.