Artificial intelligence driven lithium-ion battery recycling: from materials recovery to intelligent circular energy systems

Abstract

The rapid expansion of lithium-ion batteries (LIBs) in electric vehicles, portable electronics, and large-scale energy-storage systems has intensified concerns regarding critical material depletion, environmental impacts, and end-of-life battery management. Although conventional recycling technologies, including pyrometallurgical, hydrometallurgical, and direct recycling approaches, have enabled valuable resource recovery, their broader implementation remains challenged by heterogeneous battery chemistries and complex degradation. Artificial intelligence (AI) has emerged as a powerful tool to transform LIB recycling by enabling intelligent identification, automated sorting, predictive modelling, and adaptive process optimization. This mini-review highlights recent advances in AI-driven LIB recycling, focusing on the integration of machine learning, deep learning, and computer vision across the battery recycling value chain. The roles of AI in battery classification, recycling-route selection, material recovery prediction, and second-life assessment are critically discussed along with future perspectives toward developing autonomous and data-driven recycling ecosystems that integrate advanced materials science with artificial intelligence to accelerate the transition from conventional end-of-life battery management and recycling practices toward intelligent circular battery systems.