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In this paper, the predictive control strategy is employed to improve the current tracking performance of hub motor in 4WD electric vehicle due to its fast dynamic response. But the performance of the conventional predictive deadbeat current control suffers greatly from the parameter variations and other disturbances. Toward this, this paper presents a new predictive control strategy for hub motor; this control scheme combines an improved predictive control law with a state-observer to estimate the future motor currents and system disturbances based on a decoupled model. It provides a decoupled control of hub motor and offers stability against the variations in motor inductance and robustness against system uncertainties. The feasibility and validity of the proposed predictive current control strategy is verified through the simulation results.

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.

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.

For high torque permanent magnet wheel motor, this paper describes an experimental research method to optimize and identify the motor parameters based on the results of offline calculation. In order to improve the accuracy of motor parameters identification, the motor model considering the affect of iron loss was established, and the motor parameters were identified using genetic algorithm (GA). Based on this, parameters validation experiment was performed. The results show that: parameters obtained by this method can be used to describe the steady-state and transient-state response of permanent magnet synchronous motors accurately.

Whether high-precision torque control or motor condition monitoring need accurate motor parameters. For the three parameters of surface-mounted permanent magnet synchronous motor (SPMSM), the voltage equation is rank-deficient. To solve this problem, some scholars proposed methods that build full rank equations with signal injection, but this will produce motor torque ripple, which is not suitable for application to the EPS. Therefore, this paper proposes a method based on MRAS to identify motor parameters step by step. The proposed two steps identification method can make the reference model full rank in every step, but the total decoupling between parameters identification processes cannot be realized for the assumption that the prior step result is the real value. It was found in experiment that this effect varies with the motor operating conditions.