Search Results

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.

This paper presents an ideal force distribution control method for the electric vehicle, which is equipped with four independently in-wheel motors, in order to improve the lateral stability of the vehicle. According to the friction circle of tyre force, the ideal distribution control method can be obtained to make the front and rear wheels reach the adhesion limit at the same time in different conditions. Based on this, the force re-distributed control is applied to enhance the security of vehicle when the in-wheel motor is in the failure mode. The simulation result shows that: the force distributed method can not only improves the lateral stability of the vehicle but also enhances the vehicle safety.

Pressure following control is the basic function of Electro-Hydraulic Braking system (EHB), which is also the key technology of stability control system and regenerative braking system for hybrid and electric vehicles. Experimental research is an important method for the control and application of EHB. This paper describes a method to test and control the EHB system through experiment on the Hardware-in-the-loop (HIL) test bench and wheel motor electric vehicle. First, the HIL test bench was established, in which the EHB was tested, including the characteristics of solenoid valves and motor. Then the wheel cylinder pressure was controlled to follow the specific signal input and the master cylinder pressure. Based on this, EHB and the pressure following control method were applied to the wheel motor electric vehicle. The results show that the braking pressure can follow the driver's braking intention to realize the conventional braking function of electric vehicles.

This paper presents a torque distribution algorithm to improve the energy efficiency of four-wheel-drive (4WD) electric vehicles with PMSM hub motors. In order to optimize the torque distribution method, at first the motor model considering the affect of iron loss and the loss model of multi-motors drive system of 4WD electric vehicle with PMSM hub motors, which operate at straight-line condition, are established. Besides, realize the online identification of motor parameters based on the MARS, which is important for updating the loss model parameters of the motor drive system. By doing this, the ideal torque distribution ratio can be obtained from the loss model in real-time. The simulation result using different distribution algorithms shows that the optimized torque distribution algorithm based on the loss model can be useful for improving the energy efficiency.

Four-wheel-drive electric vehicles (4WD Evs) utilize in-wheel electric motors and Electro-Hydraulic Braking system (EHB). Then, all wheels torque can be controlled independently, and the braking pressure can be controlled more accurately and more fast than conventional braking system. Because of these advantages, 4WD Evs have potential applications in control engineering. In this paper, the in-wheel electric motors and EHB are applied as actuators in the vehicle stability control system. Based on the Direct Yaw-moment Control (DYC), the optimized wheel force distribution is given, and the coordination control of the hydraulic braking and the motor braking torque is considered. Then the EHB hardware-in-the-loop test bench is established in order to verify the effectiveness of the vehicle stability control algorithm through experiments.

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.