Battery GraphNets : Relational Learning for Lithium-ion Batteries(LiBs) Life Estimation
<|reference_start|>Battery GraphNets : Relational Learning for Lithium-ion Batteries(LiBs) Life Estimation: 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.<|reference_end|>
@article{srinivas2024battery,
    title={Battery GraphNets : Relational Learning for Lithium-ion Batteries(LiBs)
  Life Estimation},
    author={Sakhinana Sagar Srinivas, Rajat Kumar Sarkar, Venkataramana Runkana},
    journal={arXiv preprint arXiv:2408.07624},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.07624},
    primaryClass={cs.LG cs.AI}
}