ArticleNature communications2024
Generative learning assisted state-of-health estimation for sustainable battery recycling with random retirement conditions.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Circular-economy residual-value assessment of retired lithium-ion batteries via degradation prediction and game-theoretic subsidies.iScience · 2026Article
- System-level assessment of dynamic reconfiguration for lifetime and cost outcomes in electric vehicle battery packs.Nature communications · 2026Article
- A hierarchical multi‑timescale reinforcement learning framework for hybrid energy storage management with aging‑aware constraint adaptation.Scientific reports · 2026Article
- A universal protocol for ultrafast direct regeneration and upcycling of spent lithium-ion battery cathode materials.Nature protocols · 2026Review
- Contactless Battery Sensing: A Survey.Sensors (Basel, Switzerland) · 2026Review
- iMOE: prediction of second-life battery degradation trajectory using interpretable mixture of experts.Nature communications · 2026Article
- Continuous Physics-Informed Learning Expedited Battery Mechanism Decoupling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- From state estimation to active intelligence: safety-aware battery management for electric vehicles.Frontiers in chemistry · 2026Review
- 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
- Tracking business opportunities for climate solutions using AI in regulated accounting reports.Nature communications · 2025Article
- Uncovering battery electrochemical mechanisms by artificial intelligence.National science review · 2025Review
- A review of construction and sustainable recycling strategies of lithium-ion batteries across electric vehicle platforms.RSC advances · 2025Review
Corrections and comments
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Authors and funding
22 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Rapid and accurate state of health (SOH) estimation of retired batteries is a crucial pretreatment for reuse and recycling. However, data-driven methods require exhaustive data curation under random SOH and state of charge (SOC) retirement conditions. Here, we show that the generative learning-assisted SOH estimation is promising in alleviating data scarcity and heterogeneity challenges, validated through a pulse injection dataset of 2700 retired lithium-ion battery samples, covering 3 cathode material types, 3 physical formats, 4 capacity designs, and 4 historical usages with 10 SOC levels. Using generated data, a regressor realizes accurate SOH estimations, with mean absolute percentage errors below 6% under unseen SOC. We predict that assuming uniform deployment of the proposed technique, this would save 4.9 billion USD in electricity costs and 35.8 billion kg CO
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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.