A clutch control strategy is proposed to improve the shift quality of power shift transmission and solve the power cycle phenomenon in the shifting process. The strategy effectively avoids the power cycle phenomenon by analyzing the change in the transmission torque and the relationship between the rotating speed of the driving and driven disks of a shifting clutch. The evaluation indexes of the shift quality of power shift transmission are sliding friction power loss, impact degree, and torque oscillation amplitude. Particle swarm optimization (PSO), combined with a genetic algorithm, is used to optimize the shift quality of power shift transmission, solving the problem in which basic PSO frequently converges to the local optimal solution. Simulations and experiments are performed on the optimization results. The simulation and test results show that the proposed control strategy can ensure that the rotating speed of the driving disk is always higher than that of the driven disk when the clutch is insliding and wearing states. Thus, this control strategy effectively avoids power cycle when shifting and improves shift quality.

To accurately obtain the state parameter information of a vehicle, a square root generalized high-order cubature Kalman filter (CKF) estimation algorithm based on the atomic search optimization algorithm (ASO-SRGHCKF) is proposed. On the basis of the high-order CKF, using the generalized cubature rule instead of the cumbersome spherical cubature rule, the algorithm’s weights and cubature points are calculated directly. Then, the square root filtering technology is introduced, and the square root generalized high-order CKF (SRGHCKF) algorithm is derived by replacing the Cholesky decomposition with orthogonal triangle (QR) decomposition. To lessen the estimate error brought on by the noise covariance matrix’s uncertainty, the atomic search optimization (ASO) algorithm is used to optimize it, and the algorithm is utilized for the state parameter estimation of distributed electric vehicles. MATLAB/CarSim cosimulation and experiments evidence that the ASO-SRGHCKF algorithm produces more accurate estimation results and faster convergence than the HCKF algorithm and can precisely obtain the vehicle’s state parameter information.

Many tire-road friction coefficient estimation methods require the wheel angular speed sensor to provide a signal. In this paper, a tire-road friction coefficient estimation algorithm for distributed drive electric vehicles using permanent magnet synchronous motors(PMSM) sensorless control is proposed to reduce the use of wheel angular speed sensors. The wheel angular speed signal is replaced by the rotor speed signal obtained from PMSM sensorless control. First, the three degrees of freedom vehicle dynamics model and the PMSM mathematical model are established, and a strong tracking cubature Kalman filtering algorithm based on orthogonal transformation (T-STCKF) is derived to overcome the problem that the STCKF algorithm is prone to nonlocal sampling when dealing with high dimensional systems. Second, a PMSM sensorless control system based on the adaptive exponent reaching law of sliding mode control and the T-STCKF algorithm is established, and confirmed its validity. Finally, the feasibility of the proposed algorithm is demonstrated through multicase simulation and experiment and the results shows that the T-STCKF algorithm is more accurate than the STCKF algorithm.

Safe and comfortable driving experience includes the improvement of handling performance and stability control. This paper proposed a coordinated controller based on the function allocation for the handling performance and stability of distributed drive electric vehicles. The proposed controller has three layers. The upper control layer designs a dynamic stability envelope boundary suitable for various driving conditions through the phase plane method, and on this basis, the function allocation rules of torque vector control (TVC) strategy and electronic stability control (ESC) strategy were formulated. The medium control layer used the robust $H_{\infty }$ dynamic output feedback control method and the improved particle swarm optimization (PSO) parameter self-adjusting method to calculate the additional yaw moment required by the TVC strategy and the ESC strategy, respectively. The two types of additional yaw moment are implemented by the in-wheel motor and the hydraulic brake mechanism respectively. The lower control layer optimized the four-wheel torque and braking force based on the optimal tire load rate using the quadratic programming method. The proposed coordinated controller was performed in the CarSim/Simulink co-simulation platform and tested in a real vehicle platform. The results show that the proposed controller can improve the vehicle dynamic response according to the driver’s intention, thus bringing the better handling performance and stability.

In this paper, a stability control method based on active front steering control is proposed to steer by wire system (SBW). The proposed control system consists of an inner-loop angle tracking controller and an outer-loop controller. The inner-loop controller contributes to front steering angle tracking, and it is designed by PID control. The outer-loop controller restrains the effect of disturbance by feeding a compensation steering angle, and it is designed by using Improved Active Disturbances Rejection Control (IADRC) with less adjusted parameters than Active Disturbances Rejection Control (ADRC). Finally, the effectiveness of the proposed method is evaluated via numerical simulation and vehicle test. The obtained results show that the proposed controller can improve the handling stability of the SBW system. To a certain extent, the study results promote the research and application of SBW system.