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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.

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.