Universal Battery Degradation Forecasting Driven by Foundation Model Across Diverse Chemistries and Conditions

• URL: http://arxiv.org/abs/2601.00862v1
• Date: Tue, 30 Dec 2025 10:14:37 GMT
• Title: Universal Battery Degradation Forecasting Driven by Foundation Model Across Diverse Chemistries and Conditions
• Authors: Joey Chan, Huan Wang, Haoyu Pan, Wei Wu, Zirong Wang, Zhen Chen, Ershun Pan, Min Xie, Lifeng Xi,
• Abstract summary: This work proposes a unified capacity forecasting framework that maintains robust performance across diverse chemistries and usage scenarios.<n>We curate 20 public aging datasets into a large-scale corpus covering 1,704 cells and 3,961,195 charge-discharge cycle segments.<n>Experiments on both seen and deliberately held-out unseen datasets show that a single unified model achieves competitive or superior accuracy compared with strong per-dataset baselines.
• Score: 15.502149500162227
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate forecasting of battery capacity fade is essential for the safety, reliability, and long-term efficiency of energy storage systems. However, the strong heterogeneity across cell chemistries, form factors, and operating conditions makes it difficult to build a single model that generalizes beyond its training domain. This work proposes a unified capacity forecasting framework that maintains robust performance across diverse chemistries and usage scenarios. We curate 20 public aging datasets into a large-scale corpus covering 1,704 cells and 3,961,195 charge-discharge cycle segments, spanning temperatures from $-5\,^{\circ}\mathrm{C}$ to $45\,^{\circ}\mathrm{C}$, multiple C-rates, and application-oriented profiles such as fast charging and partial cycling. On this corpus, we adopt a Time-Series Foundation Model (TSFM) backbone and apply parameter-efficient Low-Rank Adaptation (LoRA) together with physics-guided contrastive representation learning to capture shared degradation patterns. Experiments on both seen and deliberately held-out unseen datasets show that a single unified model achieves competitive or superior accuracy compared with strong per-dataset baselines, while retaining stable performance on chemistries, capacity scales, and operating conditions excluded from training. These results demonstrate the potential of TSFM-based architectures as a scalable and transferable solution for capacity degradation forecasting in real battery management systems.

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