Predictive current control of permanent magnet synchronous motor for electric vehicles

Bheemolla, Anisha (2023) Predictive current control of permanent magnet synchronous motor for electric vehicles. Masters thesis, Northern Arizona University.

Text Bheemolla_2023_predictive_current_control_permanent_magnet_synchronous.pdf - Published Version Download (8MB)

Abstract

Electric vehicles (EVs) are becoming more popular and dominating internal combustion engine vehicles from a decade due to high efficiency, low emissions, high reliability, high availability, increased cost competitiveness, and improved vehicle ranges. Permanent magnet synchronous motors and two-level voltage source inverter are nowadays popularly used in the EVs. The digital control methods for traction inverter and motor improve the overall efficiency and performance of an EV and increase the range of EV for the given battery power. In the majority of commercial EVs, the linear control method such as maximum torque per ampere (MTPA) control with proportional-integral regulators and space vector modulation is a proven solution to control the interior permanent magnet synchronous motors with constant switching frequency and minimal steady-state errors. Despite the known control theory, the nonlinearities cannot be incorporated into the linear controllers. The low switching frequency operation of traction inverters lead to sluggish transient response. Moreover, proportional integral regulators are highly sensitive to the parameter variations. More advanced control methods are needed to increase the energy conversion efficiency and dynamic performance of permanent magnet synchronous motor in EV applications. Among the class of nonlinear control methods, predictive current control method gained attention in power electronics community as an attractive alternative to the classical linear control. This is due to many superior characteristics such as intuitive concept, digital controller friendliness, fast dynamic response, and ability to handle constraints and nonlinearities. Due to the absence of modulator, the switching frequency becomes variable with the predictive current control method leading to high current ripples in steady-state. To solve the variable switching frequency problem of classical predictive current control method, this thesis proposes an innovative control method named modulated model predictive current control (M2PCC). The design concepts of classical MTPA control, space vector modulation, and classical predictive current control are integrated to create the suggested control system, preserving the best characteristics from each class. The proposed M2PCC method produces fixed switching frequency operation and low current ripples in steady-state similar to that of classical MTPA control, and fast transient response similar to that of classical predictive current control method. Moreover, a matrix factorization method is developed for high-accuracy discrete-time models of motor. In contrary to the classical predictive current control method which selects optimal voltage vector of inverter as an actuation, the proposed method selects optimal sector of inverter as an actuation. The two active voltage vectors and a zero-voltage vector in the optimal sector are synthesized by the modulation stage consisting of seven-segment switching sequence. Through MATLAB simulations on an interior permanent magnet synchronous motor-based EV system, the proposed control method is validated during transient and steady-state conditions. The proposed method is also compared with the classical predictive current control method. The findings show that the proposed control method helps to meet the requirements of EV operation, including torque and speed control and current control with superior power quality. In simple words, this thesis discusses an innovative control method for EVs to improve the overall control performance and power conversion efficiency, and enable higher driving range for the given battery power.

Item Type:	Thesis (Masters)
Publisher’s Statement:	© Copyright is held by the author. Digital access to this material is made possible by the Cline Library, Northern Arizona University. Further transmission, reproduction or presentation of protected items is prohibited except with permission of the author.
Keywords:	2L-Voltage Source Inverter; Control of PMSM; Electric Vehicles; Modulated Model Predictive Current Control; MTPA; Predictive Control
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering
NAU Depositing Author Academic Status:	Student
Department/Unit:	Graduate College > Theses and Dissertations College of Engineering, Informatics, and Applied Sciences > School of Informatics, Computing, and Cyber Systems
Date Deposited:	23 Oct 2025 23:52
Last Modified:	23 Oct 2025 23:52
URI:	https://openknowledge.nau.edu/id/eprint/6275

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