AI‐Driven Detection of Lithium Plating on Graphite Anode

ABSTRACT Lithium plating deposits lithium ions as metallic lithium on the graphite anode surface instead of intercalating them into the graphite layers during charging. It occurs mainly under low‐temperature or fast‐charging conditions, causes rapid capacity fade, and in severe cases nucleates dendrites that pierce the separator and trigger thermal runaway. Conventional electrochemical methods, such as differential voltage analysis, incremental capacity analysis, and electrochemical impedance spectroscopy, detect it but are slow, condition‐sensitive, hard to deploy on board, and limited in quantifying the amount of plated lithium. Deep learning and physics‐informed machine learning read the same voltage, current, and impedance signals and recognize plating signatures directly. Reported classification accuracies cluster between 93% and 99%, and inference takes milliseconds, but these figures come from different cells, chemistries and label sources, and the millisecond figure refers to inference alone. This review covers the mechanism, the principles and representative applications of AI‐based detection, the provenance of the plating labels, and the metrics a safety‐critical, class‐imbalanced task requires.