Non-destructive degradation pattern decoupling for early battery trajectory prediction via physics-informed learning

2024-06-01

Energy & Environmental Science

AI Summary

Lithium-ion battery lifetime prediction faces challenges including data scarcity and strong heterogeneity across operating conditions and battery chemistries. To address these, this work proposes a physics-informed, non-destructive degradation pattern decoupling method. It uniquely integrates interpretable thermodynamic and kinetic parameter inversion into a neural differential equation framework, yielding a physics-informed neural network (PINN) that balances mechanistic interpretability with cross-domain generalizability. By synergistically incorporating electrochemical modeling and parameter sensitivity constraints, the method enables early-cycle lifetime trajectory prediction across diverse battery types and operating conditions using only a small amount of unlabeled cycling data. Evaluated on multi-source experimental datasets, it achieves an average trajectory prediction accuracy of 92.7% and key parameter estimation errors below 8.3%, substantially reducing experimental calibration overhead.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Planning, Routing, and Scheduling: Learning for Planning and Scheduling

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

Abstract

The paper proposes a physics-informed model to predict battery lifetime trajectories by computing thermodynamic and kinetic parameters, saving costly data that has not been established for sustainable manufacturing, reuse, and recycling.

Problem

• Predicts long-term battery degradation using limited early-cycle data
• Enables rapid residual value assessment of retired batteries
• Achieves privacy-preserving cathode material sorting for recycling

Innovation

• Physics-informed model predicts battery degradation
• Generative learning assesses residual value rapidly
• Federated learning enables privacy-preserving material sorting