With the growing global focus on environmental protection and carbon emissions, electric vehicles (EVs) are becoming increasingly popular. Permanent magnet synchronous motors (PMSMs) have emerged as a core component of the drive system due to their high-power density and compact design. The rotor temperature of PMSMs significantly affects their operating efficiency, management strategies, and lifespan. However, real-time monitoring and acquisition of rotor temperature are challenging due to cost and space limitations. Therefore, this study proposes a hybrid model named RIME-XGBoost, which integrates the RIME optimization algorithm with XGBoost, for the precise modeling and prediction of PMSM rotor temperature. RIME-XGBoost utilizes easily monitored dynamic parameters such as motor speed, torque, and currents and voltages in the d-q coordinate system as input features. It simultaneously optimizes three hyperparameters (number of trees, tree depth, and learning rate) to achieve high learning efficiency and good generalization performance. The experimental results show that, on both medium-scale datasets and small-sample datasets in high-temperature ranges, RIME-XGBoost outperforms existing methods such as SMA-RF, SO-BiGRU, and EO-SVR in terms of RMSE, MBE, R-squared, and Runtime. RIME-XGBoost effectively enhances the prediction accuracy and computational efficiency of rotor temperature. This study provides a new technical solution for temperature management in EVs and offers valuable insights for research in related fields.

This work investigated the lubricating and anti-wear properties of several sulfur additives for a nickel-based superalloy–tungsten carbide friction pair. Compared with PAO40 without any active chemical compounds, the three kinds of sulfur additives could decrease the friction coefficient from 0.2 to 0.1 and the wear volume by 90%. Sulfurized fatty acid ester had the best performance under high temperature and heavy load with COF below 0.1 and the smallest wear volume. Furthermore, the lubricating mechanism was investigated by XPS. The physical adsorptive film and the tribochemical film together enhanced the friction-reducing and anti-wear performances of the lubricants. This effective lubricant for Inconel 718 can be applied to the machining of nickel-based alloy.

The SOC estimation of UAV lithium batteries plays a crucial role in the mission planning and safe flight of UAVs. Aiming at existing UAV lithium battery SOC estimation problems, such as low estimation accuracy and poor real-time performance, a real-time online estimation scheme for UAV lithium battery SOC is proposed. A model-based approach is adopted to establish an SOC estimation model on the basis of the Thevenin equivalent circuit model, and a UAV lithium battery online monitoring device is developed to monitor the current and voltage of the UAV lithium battery in real time and import the data into the SOC estimation model in real time for calculation. Using the developed online monitoring device, the current and voltage of the lithium UAV battery are monitored in real time, and the data are imported into the SOC estimation model in real time for calculation, realizing the real-time online estimation of the SOC of the lithium UAV battery. The experimental results show that the program can realize the real-time online estimation of UAV lithium battery SOC, and the estimation error is less than 3%, which meets the requirements of online estimation accuracy of the UAV lithium battery SOC.