## 1 Introduction  
The global push for environmental sustainability has positioned electric vehicles (EVs) as crucial solutions for carbon reduction [1, 2, 3, 4]. The preferred EV propulsion technology is permanent magnet synchronous motors (PMSMs) due to their high power density and compact design [5, 6, 7]. Thermal behavior fundamentally determines PMSMs' performance [8], directly impacting operational efficiency and component longevity. This transition elevates the importance of thermal management for PMSMs. Real-world EV operation subjects the rotors in PMSMs to significant thermal stress from dynamic driving conditions: frequent torque variations, sudden load changes, and diverse terrain demands [9]. These fluctuations create critical reliability challenges through two primary mechanisms: temperature-dependent demagnetization of permanent magnets and thermal degradation of electromagnetic properties in structural materials [10]. Effective thermal modeling of rotors thus becomes essential for maintaining both the efficiency and operational safety of PMSMs under practical driving scenarios. Direct rotor temperature measurement in PMSMs faces inherent limitations, including complex sensor integration and spatial constraints [11; 12]. The temperature-dependent variation in motor parameters (e.g., rotor resistance, magnetizing inductance) introduces additional control complexity under variable loads [13]. This makes accurate thermal modeling particularly challenging. Model-based prediction methods have become increasingly adopted for PMSMs' thermal management [14]. [PERSON] et al. [15] modified the lumped-parameter model by incorporating rotor slot cooling dynamics. They improved predictions of air velocity and temperature distribution. [PERSON] et al. [16] introduced segmented stator shell modules (PSMs) and developed an LPTN model that achieves better temperature prediction accuracy under braking system failure conditions. Some advances combine Finite Element Analysis (FEA) with thermal modeling for improved prediction. [PERSON] and [PERSON] [17] developed a model-free predictive current control method enabling low-computation parameter identification. [PERSON] et al. [18] integrated signal processing with image analysis to correlate temperature data with rotor slot changes. Hybrid modeling approaches also show particular promise. [PERSON] et al. [19] combined 2D/3D thermal networks for speed-dependent temperature prediction, while [PERSON] et al. [20] enhanced computational efficiency through intelligent grid classification in magnetic-thermal field analysis. Innovations in thermal modeling encompass multiple approaches. [PERSON] et al. [21] introduced a Bidirectional LSTM (Bi-LSTM) architecture specifically designed for capturing nonlinear parameter correlations. [PERSON] et al. established temperature trends through combined loss analysis and cooling dynamics [14], while [PERSON] et al. created a 3D transient model for micro EV induction motors using time-stepping Finite Element Methods (FEMs) [22]. [PERSON] et al. [8] achieved enhanced prediction accuracy through reformulated RANS equations. While these methods demonstrate progress, critical limitations persist. Simplified models often sacrifice accuracy for speed, while loss-based estimations accumulate error propagation. Traditional FEA remains computationally intensive and data-demanding. FEMs offer computational advantages but risk instability with improper meshing. Complementing these physical models, machine learning approaches show growing potential [23]. Recent studies have explored data-driven approaches for rotor temperature prediction, demonstrating their advantages over traditional physics-based methods like Linear Parameterized Thermal Network (LPTN) models. [PERSON] et al. [24] compared machine learning models against LPTNs. Their results showed that ordinary least squares (OLSs) and shallow MLPs achieved comparable accuracy with minimal computational complexity. [PERSON] et al.