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

TL;DR

This paper introduces a universal, foundation model-based framework for battery capacity degradation forecasting that generalizes well across diverse chemistries, conditions, and datasets, outperforming traditional models.

Contribution

It presents a large-scale, unified capacity forecasting model using a Time-Series Foundation Model with physics-guided contrastive learning, capable of handling diverse battery datasets.

Findings

Single model achieves competitive or better accuracy than dataset-specific models.

Model maintains performance on unseen chemistries and operating conditions.

Framework demonstrates scalability and transferability for real battery management systems.

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.