Fast identification of the charging pile plug materials using laser-induced breakdown spectroscopy.

The electric vehicles (EVs) is showing rapid growing, with charging piles playing a critical role as essential infrastructure. The performance and reliability of charging plugs directly influence grid efficiency, while conventional copper-based materials present several limitations. Tellurium copper is an alloy well-suited for charging plugs. Identification of the materials is crucial for ensuring the electrical performance and safety, and recycling value. In this study, laser-induced breakdown spectroscopy (LIBS) was utilized for rapid identification of tellurium copper, red copper and brass. The tellurium in the alloy was identified and the LIBS parameters were optimized. K-nearest neighbor (KNN), random forest (RF), and convolutional neural networks (CNN) models were built for discrimination of three kinds of materials. Knowledge-driven feature extraction based on database and two data-driven feature extraction methods, successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS), were used to select feature bands. The optimal models achieved accuracy of 100% both for training set and testing set, indicating that the LIBS could realizing the rapid identification of charging plug materials. The proposed LIBS-based identification method helps ensure the safety and reliability of charging stations, support the healthy development of the EV industry.