CES Transactions on Electrical Machines and Systems
|
2026, 10(1): 77-86
Asymmetric Voltage Vector-Based Initial Rotor Position Detection in Four-Switch Inverter Fed BLDC Motors
Full
1 the School of Intelligent Systems Engineering, Sun Yat-Sen University, Shenzhen, Guangdong 510275, China;
2 the Guangdong Provincial Key Laboratory of Fire Science and Intelligent Emergency Technology, Guangzhou 510006, China
About Author:
Lupeng Yang: Lupeng Yang received the B.S. degree in engineering in 2021 from Wuhan University of Technology, Wuhan, China. He is currently working toward the B.S. degree in control science and engineering with the School of Intelligent Systems Engineering, Sun Yat-Sen University. His research interests include modeling, parameter identification, and control of electrical motors.
Yuting Lu: Yuting Lu (Student Member, IEEE) received the B.S. degree in engineering in 2023 from Sun Yat-sen University, Shenzhen, China, where he is currently working toward the M.S. degree in control science and engineering with the School of Intelligent Systems Engineering. His research interests include modeling, optimization, and control of electrical motors.
Guodong Feng: Guodong Feng (Senior Member, IEEE) received the B.S. and Ph.D. degrees in engineering from Sun Yat-sen University, Guangzhou, China, in 2010 and 2015, respectively. Currently, he is an Associate Professor with the School of Intelligent Systems Engineering, Sun Yat-sen University. From 2015 to 2019, he worked as a Postdoctoral Fellow with the University of Windsor, Windsor, Canada. His research interests include advanced signal processing, optimization, and electrical machines and drives. Dr. Feng is an Associate Editor of IEEE Transactions on Industrial Electronics.
Yu Han: Yu Han received the Ph.D. degree from the Department of Automation, Shanghai Jiao Tong University, Shanghai, China, in 2011. He is currently a Professor and a Ph.D. Supervisor with the School of Intelligent Systems Engineering, Sun Yat-Sen University, Guangzhou, China. He was the Chief Engineer with the Robot Business Department, Jiangsu Automation Research Institute, Lianyungang, China, and the Master Supervisor with China Ship Research and Development Academy, Wuhan, China. He has authored or coauthored a number of research papers in top journals, including Information Fusion and IEEE Transactions on Image Processing, and many authorized invention patents. His research interests include intelligent manufacturing, robotics, and manufacturing automation of marine equipment. He is the Member of “Ship and Warship Material and Related Process Technology” at China Shipbuilding Industry Group Co., Ltd, a Member of the tenth Robot and Automation Professional Committee of Welding Branch of China Construction Machinery Society, the Vice-President of Intelligent Manufacturing Industry Branch of China’s Electrical and Mechanical Engineering Association, and the Vice-Chairman of China Intelligent Manufacturing Education Alliance. He was the recipient of various national, provincial, and ministerial research projects, including the National High Technology Research and Development Program, National Key Research and Development Project, National Natural Science Foundation, and two first prizes, two second prizes, and one third prize over provincial and ministerial level.
doi: 10.30941/CESTEMS.2026.00005
In this paper, a precise and computationally efficient method for estimating multiparameter of permanent magnet synchronous motors (PMSMs) is proposed. This method can realize decoupling estimation with a small amount of data at a single speed, and considers the inductance correlation to improve the estimation accuracy. The saturation in the stator frame is first modeled, and then the related inductance model in the rotating frame is derived. The estimation model is established based on the related inductance model, which is modeled by polynomials of d-axis current (Id) for a given q-axis current (Iq). Then, the influence of permanent magnet (PM) flux linkage on inductance estimation can be eliminated by using the partial derivative of the correlated inductance model. The estimation model fully explores the inductance correlation and can realize the decoupling of PM flux linkage (λ0) and inductance, which greatly improves the inductance estimation accuracy, especially when Id is small. Moreover, this paper realizes the estimation of distortion voltage, PM flux linkage, and stator resistance based on the derived electrical model and mechanical model. Compared with the existing method, this method can use a small amount of data at a single speed to model voltage, which can effectively reduce the influence of measurement noise and improve the calculation efficiency. Experimental verification on a laboratory PMSM prototype shows that the method’s performance of the proposed method is better than existing methods under various working conditions.