Abstract

Permanent magnet synchronous motors are widely used and have sufficient development prospects in the drive systems of electric vehicles. Traditional model predictive control (MPC) methods are shown to achieve good control performance by tracking the d- and q-axis current as well as limiting the current amplitude. However, the dynamic response performance and current harmonics during the switching process are not considered in the traditional MPC. Therefore, this paper proposes an MPC that can effectively improve control performance, where the switch transfer sequence in the switch constraint module is considered in the improved model. The state transition error is obtained from the switch constraint module according to the current switch state and the transition probability, after which, the integration into the cost function in which the driving error, tracking error, and constraint error are considered. A reinforcement learning (RL) algorithm is used to obtain the weight coefficient of the transition error term in the constraint module for automatically determining the best switch state for the next control period using the cost function. Simulation tests show that the total harmonic distortion of the phase current based on the improved MPC is 978.4%, less than 2843.0% of the traditional MPC method under 20 Nm at 1000 rpm. The torque response time of the motor is reduced by 0.026 s, whereas the simulation results indicate that the 100 km acceleration performance of an electric vehicle is improved by 9.9%.

Highlights

  • Permanent magnet synchronous motors (PMSMs) are widely used owing to their small size and high efficiency, with development prospects for the drive systems of electric vehicles. e dynamic response performance and highprecision control are directly related to its control system, which require better motor control systems [1,2,3]

  • Because practical implementation can result in different parameters of the PMSM, the control characteristics generally vary with time and are nonlinear

  • The reference current and feedback current of the d- and q-axis, motor speed, and rotation angle were input into the Model predictive control (MPC) module to obtain the drive error, tracking error, and constraint error; the value of the objective function was obtained by substituting the state transition error into the cost function

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Summary

Research Article

Jun Gao ,1 Jie Zhang, Mengyang Fan, Zhiyuan Peng, Qinghong Chen, and Heshan Zhang. The dynamic response performance and current harmonics during the switching process are not considered in the traditional MPC. Erefore, this paper proposes an MPC that can effectively improve control performance, where the switch transfer sequence in the switch constraint module is considered in the improved model. E state transition error is obtained from the switch constraint module according to the current switch state and the transition probability, after which, the integration into the cost function in which the driving error, tracking error, and constraint error are considered. E torque response time of the motor is reduced by 0.026 s, whereas the simulation results indicate that the 100 km acceleration performance of an electric vehicle is improved by 9.9% Simulation tests show that the total harmonic distortion of the phase current based on the improved MPC is 978.4%, less than 2843.0% of the traditional MPC method under 20 Nm at 1000 rpm. e torque response time of the motor is reduced by 0.026 s, whereas the simulation results indicate that the 100 km acceleration performance of an electric vehicle is improved by 9.9%

Introduction
Lq ω
Reward Function
State Speed
Rotor flux Motor moment of inertia
Rolling resistance coefficient
Findings
Traditional MPC Improved MPC

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