High-fidelity Dynamic Digital Twin Modeling Method for Permanent Magnet Synchronous Motor

doi:10.16182/j.issn1004731x.joss.26-0347

Abstract

Abstract:

Conventional mechanism models often suffer from model mismatch due to the absence of nonlinear mechanical damping representation and the dynamic drift of electromagnetic parameters. To address this issue, a high-fidelity dynamic digital twin modeling method for permanent magnet synchronous motors is proposed. In the static mechanism modeling dimension, a friction energy decoupling strategy is proposed, which fundamentally overcomes the inherent conflict between steady-state accuracy and transient response from a physical logic perspective. For dynamic model updating, a distributed cascaded sensing architecture based on the multi-innovation recursive least squares and extended Kalman filter (MIRLS-MIEKF) algorithms is proposed. This architecture enables the dynamic observation of time-varying motor parameters and the real-time correction of the digital twin model. Simulation results indicate that the proposed hybrid mechanism and data-driven digital twin system achieves high-precision tracking of actual dynamic trajectories under various nonlinear operating conditions, thereby improving physical fidelity. Compared with conventional mechanism models, the mean absolute error and root mean square error of the speed trajectory are reduced by up to approximately 66% and 63.2%, respectively, providing a high-fidelity modeling foundation for the monitoring and control of PMSM.

Key words: permanent magnet synchronous motor, digital twin, high-fidelity modeling, multi-innovation theory, parameter identification

CLC Number:

TP391.9

Lai He, Yang Jinquan, Peng Cong, Li Bei. High-fidelity Dynamic Digital Twin Modeling Method for Permanent Magnet Synchronous Motor[J]. Journal of System Simulation, 2026, 38(10): 2786-2801.

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方法	R_s/Ω	L_s/mH	ψ_f/mWb
真实值	0.890	0.640 0	5.500
MIRLS-MIEKF	0.889	0.640 4	5.523
RLS	0.810	0.638 7	6.287
EKF	1.021	0.589 0	4.096
MRAS	1.011	0.639 3	4.350
SMO	1.081	0.643 6	3.605

参数	标称值
极对数p	4
额定电压U/V	24
额定电流I/A	4
额定功率P/W	64
额定转速ω/(r/min)	3×10³
转矩系数K_t/(N·m/A)	5.9×10^-2
额定转矩T/(N·m)	0.2
转动惯量J/(kg·cm²)	2.8×10^-2
阻尼系数B/(N⋅m⋅s/rad)	3.5×10^-4

性能指标	数值
实时系统仿真步长/μs	100
架构单次迭代耗时/μs	16
平台单步实际耗时/μs	49.09
最大支持采样频率/kHz	20.37

组别	方法	A	B	C	D
I	机理模型	2.732	2.555	1.894	1.356
	动态模型	1.733	1.723	1.701	0.935
	孪生模型	1.613	1.275	0.664	0.756
II	机理模型	5.867	3.481	1.829	1.861
	动态模型	4.469	2.441	1.417	1.491
	孪生模型	4.452	2.606	1.145	1.394
III	机理模型	6.916	5.664	2.904	2.951
	动态模型	14.087	3.449	1.676	1.750
	孪生模型	3.454	1.917	0.988	1.060

组别	方法	A	B	C	D
I	机理模型	34.947	16.831	6.957	7.751
	动态模型	14.892	8.997	3.599	4.773
	孪生模型	14.704	7.241	3.367	4.505
II	机理模型	28.599	12.622	6.157	6.031
	动态模型	16.354	11.742	4.359	4.549
	孪生模型	15.651	11.327	3.328	4.196
III	机理模型	26.775	20.658	9.598	10.220
	动态模型	26.712	12.529	4.089	5.169
	孪生模型	14.633	11.593	3.531	4.724