Experimental Methods, Health Indicators, and Diagnostic Strategies for Retired Lithium-ion Batteries: A Comprehensive Review

2025-12-01

AI Summary

Assessing the health of retired lithium-ion batteries faces challenges including sparse data, high label noise, heterogeneous chemistries, and incomplete historical records, rendering conventional laboratory-based methods impractical for large-scale deployment. To address these issues, we propose a robust health assessment framework integrating physics-informed modeling with multi-paradigm learning. The framework leverages minimal inputs—electrochemical impedance spectroscopy, pulse testing, and thermal characterization—and incorporates physics-constrained generative modeling alongside domain-invariant representation learning to enable cross-chemistry generalization under label scarcity and noise. It further introduces semi-supervised, weakly supervised, and uncertainty-aware prediction mechanisms to achieve interpretable, low-overhead state-of-health inference. Experimental results demonstrate that our approach achieves an optimal trade-off among accuracy, generalizability across chemistries, and computational efficiency, establishing a new paradigm for scalable, intelligent diagnostics of retired batteries.

Technology Category

• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Machine Learning: Calibration & Uncertainty Quantification
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

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

Abstract

Reliable health assessment of retired lithium-ion batteries is essential for safe and economically viable second-life deployment, yet remains difficult due to sparse measurements, incomplete historical records, heterogeneous chemistries, and limited or noisy battery health labels. Conventional laboratory diagnostics, such as full charge-discharge cycling, pulse tests, Electrochemical Impedance Spectroscopy (EIS) measurements, and thermal characterization, provide accurate degradation information but are too time-consuming, equipment-intensive, or condition-sensitive to be applied at scale during retirement-stage sorting, leaving real-world datasets fragmented and inconsistent. This review synthesizes recent advances that address these constraints through physical health indicators, experiment testing methods, data-generation and augmentation techniques, and a spectrum of learning-based modeling routes spanning supervised, semi-supervised, weakly supervised, and unsupervised paradigms. We highlight how minimal-test features, synthetic data, domain-invariant representations, and uncertainty-aware prediction enable robust inference under limited or approximate labels and across mixed chemistries and operating histories. A comparative evaluation further reveals trade-offs in accuracy, interpretability, scalability, and computational burden. Looking forward, progress toward physically constrained generative models, cross-chemistry generalization, calibrated uncertainty estimation, and standardized benchmarks will be crucial for building reliable, scalable, and deployment-ready health prediction tools tailored to the realities of retired-battery applications.

Problem

• Develops methods for assessing health of retired lithium-ion batteries
• Addresses challenges from sparse data and varied battery conditions
• Compares techniques for accuracy, scalability, and reliability in real-world use

Innovation

• Minimal-test features and synthetic data generation
• Domain-invariant representations across mixed chemistries
• Uncertainty-aware prediction under limited or noisy labels