Jinfang Hu

The hub motor of a distributed drive vehicle, as the main executing structure of the drive system, may produce varying degrees of torque loss during operation, which directly lead to the deterioration in the yaw stability of the vehicle and wheel slip especially when the vehicle is driving on low adhesion road. In this paper, a coordinated torque distribution control strategy considering wheel slip is proposed in the case of hub motor torque loss. By designing a torque constraint to prevent wheel slip and a torque distribution strategy considering torque loss, the four-wheel torque is distributed coordinately. Among the torque constraints, the fuzzy threshold method and the nonlinear feedback method are used to calculate the desired wheel torque, and then the torque constraint method considering wheel slip is designed based on particle swarm extension control theory, which can switch the torque constraint mode adaptively. To ensure the stability of the vehicle, the stability criterion is designed based on the phase plane and the yaw moment controller is designed by the sliding mode algorithm. The torque distribution strategy considering the torque loss is combined with the torque loss of the hub motor and the normal wheel torque distribution is reconstructed by the quadratic programming method. Finally, the proposed torque coordination distribution strategy is verified by simulation and hardware-in-the-loop test to prove the effectiveness of the control strategy.

When the vehicle is driving at high speed on a slippery road, the sideslip stiffness of the tire which is closely related to the lateral force of the tire, will vary with the change of road conditions, tire inflation pressure, vertical load, and other factors. If the tire sideslip stiffness is set to a fixed value in the control system based on the model design, the uncertainty of the sideslip stiffness will cause a large error with the actual application. Therefore, in order to improve the trajectory tracking accuracy of the vehicle on the slippery road surface under the premise of ensuring the stability of the vehicle, an intelligent vehicle trajectory tracking control strategy considering the influence of sideslip stiffness is designed in this paper. In order to solve the multiple constraints of the vehicle driving on the low-adhesion road surface, a trajectory tracking controller based on the multi-constraint model predictive control algorithm is designed, and then the tire sideslip stiffness estimation strategy is designed based on the XGBoost algorithm, and the estimated sideslip deviation stiffness is added to the trajectory tracking control model. Carsim and MATLAB/Simulink are used to perform co-simulation to verify the accuracy of the proposed tire sideslip stiffness estimation and the tracking performance of the trajectory tracking controller. In order to further verify the reliability of the research content, the relevant working condition tests are carried out on the hardware-in-the-loop platform based on Carsim and LabVIEW, and the effectiveness of the trajectory tracking controller considering the influence of sideslip stiffness is verified.

In this paper, an intelligent distributed drive electric vehicle is used as the research object. The research aims to solve the problem of path-tracking control of intelligent vehicles when the single-side in-wheel motor of the front axle fails. A fault-tolerant control method based on sliding mode observer is proposed. Firstly, an overall vehicle dynamics model considering the failure factor is established, and the failure of the in-wheel motor is monitored by the sliding mode observer. Torque coordination compensation control is activated according to the observed failure. The control system mainly consists of two parts: the torque-coordinated lateral stability controller and the prediction fault tolerant deviation compensation controller. The torque-coordinated lateral stability controller controls the torque of the other wheels to balance the front wheel torque, and the prediction tolerant deviation compensation controller uses Model Predictive Control (MPC) to control the front wheel angle and rear wheel torque of the vehicle to reduce the lateral deviation. Finally, under double-lane change conditions, dynamics simulations and hardware-in-loop experiments are performed to validate the effectiveness of the proposed control method.

Differential steering is a unique steering technology for distributed drive vehicles, which cannot only be applied to steering power, but also be used as a backup steering scheme for distributed drive vehicles. When the road adhesion conditions are poor, differential steering will lead to wheel slip, reduce the tire lateral force margin, and then affect the vehicle stability. To solve this issue, it is necessary to integrate the differential steering and anti-skid drive control. In this paper, the four-wheel distributed drive electric vehicle (DDEV), tire and wheel dynamics models are firstly established. Then the coordinated control strategy is proposed for anti-skid driving and differential steering of DDEV, where a neural network is adopted for the weights regulation of the two controllers. Next, an improved slip rate estimation method avoiding the calculation of vehicle speed is proposed which can reduce the error when the wheel angular acceleration is less than zero. Then, the slip rate fuzzy threshold controller is designed to control the wheel slip rate, and the feed forward-feedback differential torque controller is designed to obtain the differential torque required in steering. Considering the vehicle stability, the torque distribution adopts the quadratic programing method and combines the constraints of wheel slip rate and tire load rate to optimize the distribution of each wheel’s driving torque. Finally, the joint simulation and hardware-in-the-loop (HIL) are carried out to verify the effectiveness of the proposed improved slip rate estimation method and control strategy. The results show that the calculation error is reduced to 5.13% with the improved slip rate estimation results, and the superiority of proposed control method is verified by both simulation and HIL test.

Distributed drive vehicles are prone to wheel slip during driving on low adhesion coefficient roads. Wheel slip will not only cause energy loss, but also the different driving states of the two sides of the wheels will lead to a sharp deterioration in vehicle stability, which will adversely affect the dynamics and safety of the vehicle. For the control characteristics of distributed drive vehicles, a slip rate controller is designed on the basis of slip rate estimation, and a stability control strategy adapted to straight and steering driving are proposed. Firstly, the road surface is identified based on the Burckhardt tire model, and the optimal slip rate of the current road surface is estimated. And the optimal wheel speed corresponding to the current vehicle speed is calculated. An active disturbance rejection controller (ADRC) is established, which controls the four-wheel speeds by adjusting the motor output torque and tracks the optimal wheel speed corresponding to the optimal slip rate. The sliding-mode controller is designed considering the stability requirements of the vehicle during high-speed steering. And the wheel output torque is optimally allocated based on the quadratic programing method. Finally, joint simulations and hardware-in-the-loop tests verify the effectiveness of the control strategy proposed in this paper.

The differential torque of four in-wheel-motor drive electric automotive will affect vehicle stability, and applications of the differential driven assisting steering (DDAS) will be limited consequentially. To solve this problem, stability analysis and control system design is essential, therefore a DDAS stability control system is designed based on the layered control of yaw moment. Correlation functions are used to reflect the shifts of vehicle characteristic state between stable and unstable states, and help to determine the control weight of each subsystem in the lower-layer controller. In the lower-layer controller, the strategy of direct steering-wheel torque control is used to build a DDAS controller. Under different vehicle moving states, differential driving torque and yaw moment vary with the change of the control weights; and according to the theory of quadratic programming, optimal allocation of four-wheel driving torques will be made according to the total driving torque. The effectiveness of the proposed control system is verified by numerical simulation and hardware-in-the-loop experiment. The results show that the proposed control method can improve vehicle stability and ensure driving safety.

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.

Resource conservation has become a hot topic, and driving habits also have a significant impact on a car’s fuel consumption. In view of the different needs of drivers with different driving habits for automatic transmission and shift characteristics, a vehicle automatic transmission, and shift correction control strategy based on driving habit recognition is proposed. Based on the analysis of drivers’ driving behavior, the phase space reconstruction method is first used to reconstruct the time series of driving control signals, and the driving habit identification and gear shift correction control are conducted based on the correlation dimension and Kolmogorov entropy evaluation index. Simulation and real car test show that the identification method based on phase space reconstruction method and driving habits evaluation index can accurately identify drivers’ driving habits. The gear shift correction control strategy based on driving habit recognition fully meets the different requirements of different drivers for the gearshift performance of vehicles and improves the intelligence degree of automatic transmission of vehicles.

The autonomous vehicle consists of perception, decision-making, and control system. The study of path planning method has always been a core and difficult problem, especially in complex environment, due to the effect of dynamic environment, the safety, smoothness, and real-time requirement, and the nonholonomic constraints of vehicle. To address the problem of travelling in complex environments which consists of lots of obstacles, a two-layered path planning model is presented in this paper. This method includes a high-level model that produces a rough path and a low-level model that provides precise navigation. In the high-level model, the improved Bidirectional Rapidly-exploring Random Tree (Bi-RRT) based on the steering constraint is used to generate an obstacle-free path while satisfying the nonholonomic constraints of vehicle. In low-level model, a Vector Field Histogram- (VFH-) guided polynomial planning algorithm in Frenet coordinates is introduced. Based on the result of VFH, the aim point chosen from improved Bi-RRT path is moved to the most suitable location on the basis of evaluation function. By applying quintic polynomial in Frenet coordinates, a real-time local path that is safe and smooth is generated based on the improved Bi-RRT path. To verify the effectiveness of the proposed planning model, the real autonomous vehicle has been placed in several driving scenarios with different amounts of obstacles. The two-layered real-time planning model produces flexible, smooth, and safe paths that enable the vehicle to travel in complex environment.