Battery GraphNets : Relational Learning for Lithium-ion Batteries(LiBs) Life Estimation
This work addresses battery life estimation for optimizing performance and reliability in battery-powered systems, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackles the problem of predicting the remaining useful life of lithium-ion batteries by introducing Battery GraphNets, a framework that models relational dependencies between battery parameters to capture nonlinear degradation trajectories, achieving state-of-the-art performance on public datasets.
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.