Early prediction of battery cycle life is essential for accelerating battery research, manufacturing, and deployment. Although machine learning methods have shown encouraging results, progress is hindered by data scarcity and heterogeneity arising from diverse aging conditions. In other fields, foundation models (FMs) trained on diverse datasets have achieved broad generalization through transfer learning, but no FMs have been reported for battery cycle life prediction yet. Here we present the Pretrained Battery Transformer (PBT), the first FM for battery life prediction, developed through domain-knowledge-encoded mixture-of-expert layers. Validated on the largest public battery life database, PBT learns transferable representations from 13 lithium-ion battery (LIB) datasets, outperforming existing models by an average of 19.8%. With transfer learning, PBT achieves state-of-the-art performance across 15 diverse datasets encompassing various operating conditions, formation protocols, and chemistries of LIBs. This work establishes a foundation model pathway for battery lifetime prediction, paving the way toward universal battery lifetime prediction systems.

翻译：电池循环寿命的早期预测对于加速电池研究、制造和部署至关重要。尽管机器学习方法已展现出鼓舞人心的成果，但数据稀缺性以及不同老化条件导致的异质性阻碍了进一步进展。在其他领域，基于多样化数据集训练的基础模型（FMs）通过迁移学习实现了广泛的泛化能力，但迄今尚未有用于电池循环寿命预测的基础模型报道。本文提出预训练电池Transformer（PBT），这是首个用于电池寿命预测的基础模型，通过融合领域知识的专家混合层构建而成。在最大的公开电池寿命数据库上验证表明，PBT从13个锂离子电池（LIB）数据集中学习到可迁移的表征，其性能平均超越现有模型19.8%。通过迁移学习，PBT在涵盖不同运行条件、化成协议和锂离子电池化学体系的15个多样化数据集上均达到最先进性能。此项研究为电池寿命预测建立了基础模型路径，为构建通用电池寿命预测系统铺平了道路。