Summary of Battery Graphnets : Relational Learning For Lithium-ion Batteries(libs) Life Estimation, by Sakhinana Sagar Srinivas et al.
Battery GraphNets : Relational Learning for Lithium-ion Batteries(LiBs) Life Estimation
by Sakhinana Sagar Srinivas, Rajat Kumar Sarkar, Venkataramana Runkana
First submitted to arxiv on: 14 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel framework, Battery GraphNets, for predicting the Remaining Useful Life (RUL) of Lithium-ion Batteries (LiBs). The existing methods neglect the relational dependencies between battery parameters, which are crucial in capturing the complex degradation trajectories. The proposed method incorporates a discrete dependency graph structure to model these interactions and uses a graph-learning algorithm for RUL prognosis. Experimental results show that Battery GraphNets outperforms several popular methods on publicly available datasets, achieving state-of-the-art performance. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making better batteries by predicting when they will stop working well. Right now, we don’t have good ways to do this because we ignore how different parts of the battery work together. The new method, called Battery GraphNets, tries to fix this by looking at all these parts and how they affect each other. It’s like a map that shows how the battery is changing over time. This helps us make better predictions about when the battery will stop working well. |