An Accurate Flux Saturation Identification Method of PMaSynRM Based on Enhanced Injection Signal

The permanent magnet-assisted synchronous reluctance motor (PMaSynRM) has increasingly attracted interest in electric vehicles. However, the rapid and accurate identification of its flux saturation maps remains a challenge. Existing methods typically rely on predefined flux models, which fail to fully describe the cross-saturation characteristics of PMaSynRM, resulting in identification errors. To address this issue, this paper proposes a novel data-centric flux saturation identification method. The proposed method consists of two critical components: an enhanced injected signal and a segmented linear regression-based (SLR) data post-processing method. The proposed enhanced injected signal simultaneously meets the requirements of data point dispersion and motion suppression, enabling the generation of well-dispersed raw data points with accurate flux linkage calculation. Meanwhile, a SLR data post-processing method is designed to reduce the post-processing error. The proposed method requires no auxiliary equipment and can be executed within a short time, achieving higher flux saturation identification accuracy for PMaSynRM. The effectiveness of the proposed method is experimentally verified on two PMaSynRMs.