Battery GraphNets : Relational Learning for Lithium-ion Batteries(LiBs) Life Estimation
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866909287110934528 |
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| author | Srinivas, Sakhinana Sagar Sarkar, Rajat Kumar Runkana, Venkataramana |
| author_facet | Srinivas, Sakhinana Sagar Sarkar, Rajat Kumar Runkana, Venkataramana |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_07624 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Battery GraphNets : Relational Learning for Lithium-ion Batteries(LiBs) Life Estimation Srinivas, Sakhinana Sagar Sarkar, Rajat Kumar Runkana, Venkataramana Machine Learning Artificial Intelligence 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. |
| title | Battery GraphNets : Relational Learning for Lithium-ion Batteries(LiBs) Life Estimation |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2408.07624 |