As the electric vehicle market continues to grow, the importance of thermal management in its electric drive system has become increasingly prominent. As an integral component of the electric drive system, the temperature of permanent magnet motors directly affects the performance and safety of electric vehicles. Precise temperature monitoring and prediction of the motor, especially the stator and rotor regions with concentrated heat generation, are crucial to preventing overheating motor damage. However, it is hard to seize the temperature by sensors simply. This paper proposes a datadriven method for predicting the stator and rotor temperatures of PMSMs. Firstly, based on the studied form-wound PMSM, a simulation model of the motor is established, and the corresponding losses are calculated. Secondly, a thermal network model of the motor is constructed using the calculated losses, which enables the analysis of temperature distribution and trends within the motor. Then, based on the thermal network model, a data-driven model is constructed using a CNN-LSTM architecture. Ultimately, to verify the precision of the predictive model, simulation and experimental tests are conducted, wherein the model's projections are juxtaposed against actual motor data derived from experiments. The results show that the average error of stator temperature is 4.3 °C, and the maximum error is 6.7 °C. The average temperature error of the rotor permanent magnet is 3.9 °C, and the maximum error is 6.2 °C. In comparison with existing methodologies, the estimation accuracy has been enhanced by a minimum of 25%. The prediction outcomes exhibit high fidelity, thereby furnishing an efficacious approach for forecasting the temperature of PMSMs.

The multi-granular probabilistic linguistic modeling allows decision makers to express cognitive information using multiple linguistic term sets based on their preferences. However, personalized individual semantics (PIS) can lead to different meanings of the same word within the linguistic context. To address this issue and manage consensus in large-scale group decision making , this study proposes a decision framework that employs multi-granular probabilistic linguistic preference relations (MGPLPRs). First, a transformation method is presented to unify different granularity levels of MGPLPRs, thus ensuring the consistency of granularity. Moreover, a consistency-driven optimization model is constructed to generate the numerical scales with PIS for different experts. Thereafter, a two-stage consensus reaching process (CRP) is developed, including both within-cluster and across-cluster CRP, to achieve group consensus. The experts’ original weights are derived from a social network, taking into account the trust relationships among them. A dynamic weighting mechanism is used to update the experts’ weights based on their contributions to group consensus, which better reflects the actual situation than fixed weights. The proposed method is exemplified through a case study of assessing and selecting campus surveillance measures for COVID-19. Finally, the effectiveness and robustness of the proposed framework are verified through comparative analysis and sensitivity analysis.