US 12,348,160 B2
Efficiency optimization control method and system for permanent magnet synchronous motor
Siqi Peng, Xiangtan (CN); Weijun Li, Changsha (CN); Dan Guo, Chenzhou (CN); Hongyu Peng, Zhuzhou (CN); Hui Li, Xiangtan (CN); and Pingan Tan, Xiangtan (CN)
Assigned to XIANGTAN UNIVERSITY, Xiangtan (CN); FOSHAN GREEN INTELLIGENT MANUFACTURING RESEARCH INSTITUTE OF XIANGTAN UNIVERSITY, Foshan (CN); and FOSHAN SHUNDE LEPUDA MOTOR CO., LTD, Foshan (CN)
Filed by Xiangtan University, Xiangtan (CN); Foshan Green Intelligent Manufacturing Research Institute Of Xiangtan University, Foshan (CN); and FOSHAN SHUNDE LEPUDA MOTOR CO., LTD, Foshan (CN)
Filed on Aug. 30, 2023, as Appl. No. 18/239,994.
Application 18/239,994 is a continuation of application No. PCT/CN2023/090074, filed on Apr. 23, 2023.
Claims priority of application No. 202210542923.4 (CN), filed on May 18, 2022.
Prior Publication US 2023/0412098 A1, Dec. 21, 2023
Int. Cl. H02P 21/00 (2016.01); H02P 21/14 (2016.01)
CPC H02P 21/0003 (2013.01) [H02P 21/14 (2013.01)] 14 Claims
OG exemplary drawing
 
1. An efficiency optimization control method for permanent magnet synchronous motor, comprising:
step 1: obtaining a suboptimal direct axis (d-axis) current id of a permanent magnet synchronous motor by using a loss model algorithm;
step 2: performing, by using the suboptimal d-axis current as an initial value and using a deep reinforcement learning algorithm, an optimizing process on the suboptimal d-axis current ia to construct an optimal deep reinforcement learning model; and
step 3: inputting currently acquired state data of the permanent magnet synchronous motor into the optimal deep reinforcement learning model to obtain a control parameter value corresponding to an optimal efficiency of the permanent magnet synchronous motor, and controlling the permanent magnet synchronous motor based on the control parameter value;
wherein in the step 3, the control parameter value comprises an optimal d-axis current and an optimal q-axis current; and the step 3 comprises:
predicting the optimal d-axis current making the permanent magnet synchronous motor run with the optimal efficiency based on the optimal deep reinforcement learning model; and
controlling the permanent magnet synchronous motor based on the optimal d-axis current, and compensating a q-axis current of the permanent magnet synchronous motor based on the optimal d-axis current to thereby achieve an optimal control for efficiency of the permanent magnet synchronous motor in a steady state, wherein a formula of a q-axis current variation Δiq is expressed as follows:

OG Complex Work Unit Math
wherein Ld represents a d-axis armature inductance, and Lq represents a q-axis armature inductance; ψm represents a magnetic linkage of a rotor; id represents a d-axis current before adjusting, and Δid is a d-axis current variation after adjusting.