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.
Cite
@article{arxiv.2408.07624,
title = {Battery GraphNets : Relational Learning for Lithium-ion Batteries(LiBs) Life Estimation},
author = {Sakhinana Sagar Srinivas and Rajat Kumar Sarkar and Venkataramana Runkana},
journal= {arXiv preprint arXiv:2408.07624},
year = {2024}
}
Comments
Accepted in Workshop on Graph Learning for Industrial Applications : Finance, Crime Detection, Medicine, and Social Media (NeurIPS 2022)