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

• URL: http://arxiv.org/abs/2408.07624v1
• Date: Wed, 14 Aug 2024 15:44:56 GMT
• Title: Battery GraphNets : Relational Learning for Lithium-ion Batteries(LiBs) Life Estimation
• Authors: Sakhinana Sagar Srinivas, Rajat Kumar Sarkar, Venkataramana Runkana,
• Abstract summary: We present the Battery GraphNets framework that jointly learns to incorporate a discrete dependency graph structure between battery parameters. The proposed method outperforms several popular methods by a significant margin on publicly available battery datasets and achieves SOTA performance.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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