Abstract
This paper presents an innovative approach to the multi-objective design optimization of a six-phase, 12/10 Fractional Slot Concentrated Winding (FSCW) Permanent Magnet Synchronous Machine (PMSM), with an emphasis on fault tolerance across varied load conditions. Traditional research in multiphase PMSM optimization has predominantly concentrated on control aspects, with minimal exploration of the design optimization process. Addressing this research gap, this paper incorporates both healthy and fault conditions, including single-phase open-circuit faults, besides two prevalent operational strategies: the Minimum Loss Strategy (MLS) and Maximum Torque Strategy (MTS). To effectively simulate the operational challenges in electric vehicles (EVs), data mining and clustering techniques are utilized to analyze and comprehend real-world drive cycle data. The design and optimization process are underpinned by Finite Element Analysis (FEA), employing ANSYS MAXWELL for the design phase and ANSYS OptiSlang for the multi-objective optimization. Simulations are based on a practical power level of 50 kW for EV applications, while experimental validation is carried out using a lab-scale 2 kW motor. The findings underscore the critical role of fault tolerance consideration in the design phase, which enhances the robustness and adaptive performance of multiphase PMSMs to meet the condition of diverse load conditions. This research potentially sets a novel technique in PMSM design, steering towards machines that are not only performance-optimized but also inherently resilient to operational abnormalities.