ArticleNature communications2023
Collaborative and privacy-preserving retired battery sorting for profitable direct recycling via federated machine learning.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
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Who cites it
17 citing papers in PubMed.
- Spatial mismatches constrain high-value utilization of retired batteries for decarbonization in China.Environmental science and ecotechnology · 2026Article
- Recycling nickel-rich cathodes toward structural and functional circularity: a perspective.Chemical science · 2026Review
- A universal protocol for ultrafast direct regeneration and upcycling of spent lithium-ion battery cathode materials.Nature protocols · 2026Review
- Efficient Regeneration of Degraded LiNiNanomaterials (Basel, Switzerland) · 2026Article
- X-ray fluorescence spectroscopy for rapid identification of cathode chemistry in lithium-ion battery recycling.Communications engineering · 2026Article
- iMOE: prediction of second-life battery degradation trajectory using interpretable mixture of experts.Nature communications · 2026Article
- Privacy-preserving collaborative battery fault warning for massive electric vehicles by heterogeneous data from charging stations.Nature communications · 2025Article
- Deep learning predicts real-world electric vehicle direct current charging profiles and durations.Nature communications · 2025Article
- Unlocking vehicle-to-grid potential of load shifting in China's megacities considering comprehensive real-world behaviors.Nature communications · 2025Article
- Uncovering battery electrochemical mechanisms by artificial intelligence.National science review · 2025Review
- A forewarning model for the reverse supply chain of urban End-of-Life power batteries based on a mix method of BWM and RBFNN.Scientific reports · 2025Article
- Efficient estimating and clustering lithium-ion batteries with a deep-learning approach.Communications engineering · 2025Article
- RecyBat24: a dataset for detecting lithium-ion batteries in electronic waste disposal.Scientific data · 2025Article
- Generative learning assisted state-of-health estimation for sustainable battery recycling with random retirement conditions.Nature communications · 2024Article
- Machine learning-based design of electrocatalytic materials towards high-energy lithium||sulfur batteries development.Nature communications · 2024Article
- Pathway decisions for reuse and recycling of retired lithium-ion batteries considering economic and environmental functions.Nature communications · 2024Article
- An extended substrate screening strategy enabling a low lattice mismatch for highly reversible zinc anodes.Nature communications · 2024Article
Corrections and comments
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Authors and funding
17 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.