A novel hybrid approach towards drive-cycle based design and optimization of a fractional slot concentrated winding SPMSM for BEVs

Abstract

Research conducted previously has shown that a battery electric vehicle (BEV) motor design incorporating drive-cycle optimization can lead to achievement of a higher torque density motor that consumes less energy over the drive-cycle in comparison to a conventionally designed motor. Such a motor indirectly extends the driving range of the BEV. Firstly, in this paper, a baseline fractional slot concentrated winding (FSCW) surface permanent magnet synchronous machine (SPMSM) designed for a direct-drive BEV utilizing the conventional machine design approach has been developed. A vehicle dynamics model for the baseline machine and its associated vehicle parameters are used against an urban dynamometer driving schedule (UDDS) to derive loading data in terms of torque, speed, and energy. Energy Center of Gravity (ECG) and K-means clustering are two existing methods for reducing the number of machine operating points of the drive-cycle while preserving the characteristics of the entire cycle are implemented, which offer high computational efficiency and low computational time cost while optimizing an electric machine. Understanding the merits and demerits of the two existing methods, a novel hybrid approach of drive-cycle data representation is proposed. The drive-cycle data elicited from all of the approaches are thereafter used towards optimization of FSCW SPMSMs. Finally, a comparative performance analysis of optimally designed SPMSMs using the two existing approaches and the proposed approach is conducted.