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

Shengyu Tao, Haizhou Liu, Chongbo Sun, Haocheng Ji, Guanjun Ji, Zhiyuan Han, Runhua Gao, Jun Ma, Ruifei Ma, Yuou Chen, Shiyi Fu, Yu Wang, Yaojie Sun, Yu Rong, Xuan Zhang (), Guangmin Zhou () and Hongbin Sun ()
Additional contact information
Shengyu Tao: Tsinghua University
Haizhou Liu: Tsinghua University
Chongbo Sun: Tsinghua University
Haocheng Ji: Tsinghua University
Guanjun Ji: Tsinghua University
Zhiyuan Han: Tsinghua University
Runhua Gao: Tsinghua University
Jun Ma: Tsinghua University
Ruifei Ma: Tsinghua University
Yuou Chen: Tsinghua University
Shiyi Fu: Fudan University
Yu Wang: Fudan University
Yaojie Sun: Fudan University
Yu Rong: Tencent AI Lab, Tencent
Xuan Zhang: Tsinghua University
Guangmin Zhou: Tsinghua University
Hongbin Sun: Tsinghua University

Nature Communications, 2023, vol. 14, issue 1, 1-14

Abstract: 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 under homogeneous and heterogeneous battery recycling settings respectively, attributed to our innovative Wasserstein-distance voting strategy. Economically, the proposed method underscores the value of precise battery sorting for a prosperous and sustainable recycling industry. This study heralds a new paradigm of using privacy-sensitive data from diverse sources, facilitating collaborative and privacy-respecting decision-making for distributed systems.

Date: 2023
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DOI: 10.1038/s41467-023-43883-y

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