# Degradation-Aware Self-Supervised Learning with Interpretable Battery-State Representations for Label-Efficient Battery Health Diagnostics

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/degradation-aware-self-supervised-learning-with-interpretable-battery-state/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Ji Young Yun,Haechang Kim,Jong Min Lee |
| Citations | 0 |
| DOI | 10.2139/ssrn.6957938 |
| Fields | Engineering |
| Open Access | true |
| OA Status | green |
| OA URL | https://doi.org/10.2139/ssrn.6957938 |
| OpenAlex ID | https://openalex.org/W7167152791 |
| Type | preprint |
| Year | 2026 |

## Paper authors

- [Haechang Kim](https://scholariq.org/researchers/haechang-kim/)

## Paper journal

- [SSRN Electronic Journal](https://scholariq.org/journals/ssrn-electronic-journal/)

## Paper primary topic

- [Advanced Battery Technologies Research](https://scholariq.org/topics/advanced-battery-technologies-research/)

## Paper topics

- [Advanced Battery Technologies Research](https://scholariq.org/topics/advanced-battery-technologies-research/)
- [Advancements in Battery Materials](https://scholariq.org/topics/advancements-in-battery-materials/)
- [Advanced Battery Materials and Technologies](https://scholariq.org/topics/advanced-battery-materials-and-technologies/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.