Adaptive Digital Twin Framework for PMSM Thermal Safety Monitoring: Integrating Bayesian Self-Calibration with Hierarchical Physics-Aware Network

To address the limitations of parameter drift in physical models and poor generalization in data-driven methods, this paper proposes a self-evolving digital twin framework for PMSM thermal safety. The framework integrates a dynamic-batch Bayesian calibration (DBBC) algorithm and a hierarchical physics-aware network (HPA-Net). First, the DBBC eliminates plant–model mismatch by robustly identifying stochastic parameters from operational data. Subsequently, the HPA-Net adopts a “physics-augmented” strategy, utilizing the calibrated physical model as a dynamic prior to directly infer high-fidelity temperature via a hierarchical training scheme. Furthermore, a real-time demagnetization safety margin (DSM)…

• NN
  National Natural Science Foundation of China

  Awards: 62503250, 62503388
• FR
  Fundamental Research Funds for the Central Universities

  Award: G2024KY05104