mlsanityData to 13 Oct 2026

Battery GraphNets : Relational Learning for Lithium-ion Batteries(LiBs) Life Estimation

arXiv 2408.07624 ↗First posted 14 Aug 2024cs.LG cs.AIcited by 0

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Abstract (arXiv)

Battery life estimation is critical for optimizing battery performance and guaranteeing minimal degradation for better efficiency and reliability of battery-powered systems. The existing methods to predict the Remaining Useful Life(RUL) of Lithium-ion Batteries (LiBs) neglect the relational dependencies of the battery parameters to model the nonlinear degradation trajectories. We present the Battery GraphNets framework that jointly learns to incorporate a discrete dependency graph structure between battery parameters to capture the complex interactions and the graph-learning algorithm to model the intrinsic battery degradation for RUL prognosis. The proposed method outperforms several popular methods by a significant margin on publicly available battery datasets and achieves SOTA performance. We report the ablation studies to support the efficacy of our approach.

Key facts. No citing papers in the map yet (data through 13 Oct 2026). Builds on 50 of its 88 references that are in the map. First posted on arXiv 14 Aug 2024.

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