A novel decoupling control method based on neural network for EV’s Driving PMSM

Abstract

Permanent Magnet Synchronous Motor (PMSM) is often used in Electrified Vehicle (EV), while its Id (direct-axis current) and Iq (quadrature-axis current) are coupled. Traditional FOC (Field Oriented Control) method can’t get an accurate decoupling control of them when the motor speed changed with the vehicle’s driving condition, especially at high speed. This coupling issue leads an unstable torque output. And further, it deteriorates the NVH (noise vibration and harshness) performance of EV. This paper focuses on the decoupling solution and puts forward a new control strategy which combines the neural network control idea with FOC method. The novel method gives a neural network controller based on four single neuron PID controllers, which its function is to realize the d-q axis interacted adjustment and decoupling. Four single neurons PID controllers achieve the negative feedback control of Id to Ud (direct-axis voltage), Iq to Ud, Iq to Uq (quadrature-axis voltage), and Id to Uq respectively. Creatively, it takes PMSM speed as one of the neuron inputs to adjust the feedback weight of Id and Iq dynamically. A comparing simulation which is set up in the Simulink platform is given in this paper. Simulation results show that this method gives a good self-adaptiveness and decouples the influence of Id and Iq, as well as improve the motor control quality at high speed.