The tendency of metaheuristic algorithms to converge prematurely during surface-mounted permanent magnet synchronous motor (SPMSM) parameter identification compromises their accuracy. To address this issue, this paper proposes an improved eel and grouper optimization (IEGO) algorithm. Building on the standard EGO, IEGO implements three key enhancements. First, an elite dynamic reverse learning strategy during initialization elevates initial population quality and search efficiency. Second, the integration of the sparrow search algorithm surveillance mechanism with adaptive normal cloud modeling optimizes position-following strategies to improve global exploitation capability and convergence speed. Lastly, the incorporation of Tent chaotic mapping strengthens local optima avoidance to prevent premature convergence and boost global exploration. During parameter identification, the measured motor signals (current, voltage, and speed) are processed by the identification model, wherein the IEGO algorithm performs iterative optimization using a fitness function, enabling rapid, high-precision identification of critical parameters, including stator resistance, d/q-axis inductances, and permanent magnet flux linkage. Simulation and experimental results confirm the significant advantages of IEGO in SPMSM parameter identification, demonstrating superior accuracy, enhanced convergence stability, and reliable parameter foundations for precision motor control.
Yao et al. (Thu,) studied this question.