A High-Precision Parameter Identification Framework for Permanent Magnet Synchronous Traction Motors in Urban Rail Trains

The high-precision parameter identification of permanent magnet synchronous traction motors (PMSTMs) is critical to achieving optimal control performance for urban rail trains. Traditional chaotic particle swarm optimization (CPSO) algorithms often suffer from premature convergence to local optima due to parameter coupling, dynamic sensitivity, and multiparameter trade-offs under complex operating conditions, thereby limiting the performance of the algorithm. To address these challenges, this article proposes a perturbed PMSTM model incorporating time-varying stator resistance and flux linkage disturbances, which better aligns with industrial realities such as temperature fluctuations and magnetic saturation. Furthermore, a novel parameter identification algorithm has been designed: 1) chaotic logistic mapping for inertia weight adaptation to enhance uniform global exploration; 2) adaptive mutation strategy with dynamic probability adjustment based on fitness evolution to balance sensitivity differences among parameters; and 3) simulated annealing (SA) mechanism with metropolis criterion to resolve multiparameter conflicts and escape local optima. A comprehensive comparative validation using the constructed hardware-in-the-loop (HIL) platform demonstrates the effectiveness and superiority of the framework in achieving high-precision parameter identification for PMSTM, which provides a practical solution for enhancing the efficiency and reliability of urban rail traction systems.