Collaborative and privacy-preserving retired battery sorting for profitable direct recycling via federated machine learning.

ABSTRACT: Unsorted retired batteries with varied cathode materials hinder the adoption of direct recycling due to their cathode-specific nature. The surge in retired batteries necessitates precise sorting for effective direct recycling, but challenges arise from varying operational histories, diverse manufacturers, and data privacy concerns of recycling collaborators (data owners). Here we show, from a unique dataset of 130 lithium-ion batteries spanning 5 cathode materials and 7 manufacturers, a federated machine learning approach can classify these retired batteries without relying on past operational data, safeguarding the data privacy of recycling collaborators. By utilizing the features extracted from the end-of-life charge-discharge cycle, our model exhibits 1% and 3% cathode sorting errors un

SUBMITTER: Tao S 

PROVIDER: S-EPMC10697957 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature