Abstract
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. This work establishes a foundation model pathway for battery lifetime prediction, paving the way toward universal battery lifetime prediction systems.
Abstract (translated)
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经取得了令人鼓舞的结果，但由于不同老化条件导致的数据稀缺性和异质性，这一领域的进步受到了阻碍。在其他领域，通过多样化的数据集训练的基础模型（FMs）已通过迁移学习实现了广泛泛化，但尚未有关于电池循环寿命预测的基础模型的报道。在这里，我们介绍了预训练电池变换器（PBT），这是首个用于电池寿命预测的基础模型，它是通过编码领域知识的专家混合层开发出来的。在最大的公共电池寿命数据库上进行了验证，PBT从13个锂离子电池(LIB)数据集中学习可转移表示形式，在性能上比现有模型平均高出19.8%。利用迁移学习，PBT在包含各种工作条件、形成协议和化学成分的15种多样化的数据集上实现了最先进的性能。这项研究为电池寿命预测的基础模型路径奠定了基础，并开辟了向通用电池寿命预测系统发展的道路。
URL
https://arxiv.org/abs/2512.16334
