Generative learning assisted state-of-health estimation for sustainable battery recycling with random retirement conditions

2024-11-23

Nature Communications

AI Summary

Accurately estimating the state of health (SOH) of retired electric vehicle batteries for second-life applications remains challenging—particularly under real-world recycling conditions characterized by limited labeled samples, high sensor noise, non-uniform degradation patterns, and label scarcity—where existing methods lack robustness and hinder large-scale deployment in energy-constrained regions. To address this, we propose a physics-informed conditional diffusion graph neural network framework that pioneers generative learning for retired battery aging modeling, integrated with transfer learning and epistemic uncertainty quantification. Evaluated on a real-world retired battery dataset, our method reduces SOH estimation error by 42% and improves prediction confidence by 3.1× compared to state-of-the-art baselines. This significantly enhances battery sorting efficiency and second-life economic viability, offering a scalable, reliable technical foundation for sustainable battery circular economy initiatives.

Technology Category

• Intelligent Robots: State Estimation
• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Graphical Models

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Problem

• Diagnostics of second-life EV batteries.
• Performance and safety uncertainties addressed.
• Scalable solutions for energy storage.

Innovation

• Multidimensional rapid pulse test
• Second-life battery diagnostics
• Comprehensive dataset creation