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To reduce the fuel consumption of an intelligent four-wheel-drive (4WD) electric vehicle (EV), this paper presents a new method of speed trajectory planning. The proposed method can realize a fast real-time optimization of vehicle speed, aiming to achieve the minimum motor energy output according to the fuel consumption directly. In addition, the optimization method maintains the cruising speed within the deviation required to achieve a good control effect. First, the road slope information is considered, and then, a 4WD EV longitudinal dynamic prediction model and a fuel consumption function are established. Next, the state and control variables are chosen to establish the cost function; in this manner, the MPC optimization problem in each prediction horizon is transformed into quadratic form. Finally, the fast solving tool called GRAMPC is used to solve the MPC problem.

At present, most of the game decision lane-changing models only consider the state data of the vehicle at the current moment. However, the driving style has a significant impact on the vehicle trajectories, which should be taken into account in the lane-changing process. Moreover, most of the game models are static and do not take into account the sequence of the vehicle lane-changing. This paper proposed a Stackelberg-game-based vehicle lane-changing model considering driving style. Firstly, the NGSIM public dataset is selected for this research and the data screen flow is processed. The K-means algorithm is applied to exchange data clustering. Based on the analysis of vehicle lane changing features under different driving style, the characteristics of the corresponding data under different style are extracted. The quantic-polynomial programming algorithm is used to generate a vehicle lane changing trajectory under different driving styles.

This paper presents a low-speed assisted steering control approach for distributed drive electric vehicles. When the vehicle is driven at low speed, the braking of the inner-rear wheel is combined with differential drive to reduce the turning radius. A hierarchical control structure has been designed to achieve comprehensive control. The upper-level controller tracks the expected yaw rate and vehicle side-slip angle through a Linear Quadratic Regulator (LQR) algorithm. The desired yaw rate and vehicle side-slip angle are obtained according to the reference vehicle model, which can be regulated by the driver through the accelerator pedal. The lower-level controller uses a quadratic programming algorithm to distribute the yaw moment and driving moment to each wheel, aiming to minimize tire load rate variance.