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

arXiv: https://arxiv.org/abs/2512.01294 · HTML: https://mlsanity.com/paper/2512.01294/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 197 references that are in the map.
- First posted on arXiv 1 Dec 2025.

## Builds on (most cited first, 4 of 4)

- [Physics-Informed Neural Networks for Prognostics and Health Management of Lithium-Ion Batteries](https://mlsanity.com/paper/2301.00776/) (2023)
- [PulseBat: A field-accessible dataset for second-life battery diagnostics from realistic histories using multidimensional rapid pulse test](https://mlsanity.com/paper/2502.16848/) (2025)
- [Optimal battery thermal management for electric vehicles with battery degradation minimization](https://mlsanity.com/paper/2308.03056/) (2023)
- [Taking Second-life Batteries from Exhausted to Empowered using Experiments, Data Analysis, and Health Estimation](https://mlsanity.com/paper/2402.18859/) (2024)

## Abstract (arXiv)

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.