Multi-Vector Switching Based Model Predictive Current Control for Five-Phase PMSHM with Optimal Virtual Vector Duty Cycle Optimization

With the growing demand for high-performance electric motors in various applications, particularly in electric vehicles, the need for advanced control strategies is critical. Model Predictive Current Control (MPCC) has emerged as an effective solution for addressing current ripple, dynamic performance, and efficiency. This paper presents a multi-vector switching model predictive current control (MVS-MPCC) method for a five-phase permanent magnet synchronous hub motor based on optimal virtual vector duty cycle optimization. Firstly, a multi-vector switching method is proposed and an automatic switching area is set to solve the problem of poor steady-state performance in single-vector methods and high switching frequency in multi-vector methods. This approach enhances steady-state control performance at a specified switching frequency, maintains a quick dynamic response, and reduces computational complexity. Secondly, an optimal duty cycle optimization method based on virtual voltage vectors is proposed. This technique maintains the benefit of eliminating harmonic voltage while enhancing DC voltage utilization and expanding the speed range. Additionally, the method exclusively uses large vectors to reduce common mode voltage. Finally, the effectiveness and feasibility of the proposed scheme are verified on the experimental platform by comparing it with existing methods.