Physics-Informed Deep Learning for Dynamic Friction Coefficient Prediction in the Pantograph–Catenary System Under Complex Current-Carrying Conditions

The pantograph–catenary system is the critical pathway for energy collection for high-speed trains, and its interfacial state directly affects current-collection quality and operational safety. Due to the coupled effects of multiple factors, the friction coefficient at the interface exhibits significant nonlinearity, time variability, and stochastic fluctuations, posing substantial challenges for friction-behavior prediction. To improve the prediction accuracy and generalization capability of friction-coefficient models under complex current-carrying conditions, a CNN-LSTM model optimized by a physics-informed Sparrow Search Algorithm, namely PISSA-CNN-LSTM, is proposed in this study. Based on current-carrying friction tests, the effects of current, contact load, and sliding speed on the dynamic evolution of the friction coefficient are analyzed. The physics-based regularities associated with operating conditions are further incorporated into the SSA-based hyperparameter optimization process, enabling directed optimization under physical constraints. The results show that PISSA-CNN-LSTM outperforms CNN-LSTM and SSA-CNN-LSTM in prediction accuracy, convergence speed, and optimization efficiency. The test-set R2 reaches 0.9904, and the optimization time is reduced by 49.38% compared with SSA-CNN-LSTM. This work provides a more accurate, robust, and interpretable modeling approach for predicting pantograph–catenary interfacial friction behavior under complex current-carrying conditions.