BatteryMFormer: Multi-level Learning for Battery Degradation Trajectory Forecasting
The article presents BatteryMFormer, a novel approach for forecasting battery degradation trajectories. This method addresses the unique characteristics of battery degradation data, which include multi-level structures and localized variations in voltage-current profiles. Extensive experiments demonstrate that BatteryMFormer outperforms existing state-of-the-art methods in this domain.
- ▪BatteryMFormer is designed for early battery degradation trajectory forecasting (BDTF).
- ▪The model incorporates an aging-condition-aware decoder and a meta degradation pattern memory.
- ▪Experiments show that BatteryMFormer consistently outperforms existing methods in battery degradation forecasting.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.27044 |
| Publication time | Wed, 27 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-27T04:07:56.398Z |
| Last seen | 2026-05-27T04:07:56.398Z |
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Computer Science > Artificial Intelligence arXiv:2605.27044 (cs) [Submitted on 26 May 2026] Title:BatteryMFormer: Multi-level Learning for Battery Degradation Trajectory Forecasting Authors:Ruifeng Tan, Jintao Dong, Weixiang Hong, Jia Li, Jiaqiang Huang, Tong-Yi Zhang View a PDF of the paper titled BatteryMFormer: Multi-level Learning for Battery Degradation Trajectory Forecasting, by Ruifeng Tan and 5 other authors View PDF HTML (experimental) Abstract:Early battery degradation trajectory forecasting (BDTF), which predicts the full-life state-of-health trajectory from early operational data, is critical for battery optimization, manufacturing, and deployment. Battery degradation data exhibit two key characteristics.
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