Summary of Adapting Amidst Degradation: Cross Domain Li-ion Battery Health Estimation Via Physics-guided Test-time Training, by Yuyuan Feng et al.
Adapting Amidst Degradation: Cross Domain Li-ion Battery Health Estimation via Physics-Guided Test-Time Training
by Yuyuan Feng, Guosheng Hu, Xiaodong Li, Zhihong Zhang
First submitted to arxiv on: 30 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers develop a novel framework to improve state-of-health estimation in lithium-ion batteries. The framework, called BatteryTTT, uses test-time training to adapt the model as new data is collected during degradation experiments, reducing the time-consuming nature of data collection. Additionally, the authors integrate physical laws into self-supervised learning and explore the potential of large language models in battery sequence modeling. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Lithium-ion batteries are important for energy management and have significant social and economic implications. Researchers want to accurately estimate their state-of-health (SOH) but struggle with a lack of high-quality data. To solve this problem, scientists introduce a new framework called BatteryTTT that uses test-time training. This means the model gets better as it sees more data from the battery. The authors also use physical laws to help the model learn and try using large language models for SOH estimation. |
Keywords
* Artificial intelligence * Self supervised
