The pantograph–catenary system (PCS) is a critical component through which electrified railway trains obtain electrical energy, and the current-carrying friction and wear behavior at the pantograph–catenary interface directly affect current collection quality and operational safety. Water environments, particularly icing conditions, may induce contact instability, arc ablation, and abnormal wear. Therefore, this paper provides a comprehensive review of research progress on the current-carrying friction and wear behavior of C/Cu contact pairs under water environments. It focuses on the interfacial evolution characteristics under three different phase states of water and analyzes their influence mechanisms on lubrication conditions, current transmission, and wear behavior. Typical protection strategies, including speed restriction, mechanical de-icing, thermal de-icing, and anti-icing measures, are summarized, and their applicability and current development status are discussed. Finally, it is suggested that future research should focus on the development of high-performance carbon strip materials, accurate monitoring of ice morphology and types, and efficient hybrid anti-/de-icing technologies, thereby ensuring the reliable operation of high-speed railways under icing 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.