Pretrained battery transformer (PBT): A foundation model for battery life prediction

Abstract

Early prediction of battery cycle life is essential for improving battery design, manufacturing and deployment. However, despite encouraging progress with machine learning, battery life prediction remains constrained by scarce data and pronounced heterogeneity across battery chemistries, specifications, formation protocols and operating conditions. Although transfer learning has been widely explored to alleviate these challenges, its effectiveness is limited by the absence of a foundation model that can integrate heterogeneous battery life data and provide broadly useful knowledge for target-scenario specialization. Here we introduce the pretrained battery transformer (PBT), an integrated foundation model comprising a general PBT and specialized PBT models for individual target scenarios. At its core, battery-knowledge-encoded mixture-of-experts layers enable the general PBT to consolidate shared cycling-pattern–lifetime relationships from 13 heterogeneous lithium-ion battery datasets while preserving specialization across distinct aging regimes. The resulting general PBT provides a shared knowledge and parameter foundation from which specialized PBT models are constructed using limited labelled data to capture target-specific degradation behavior. Across 15 downstream datasets covering 977 batteries and 532 aging conditions from lithium-ion, sodium-ion and zinc-ion batteries, the specialized PBT models achieve state-of-the-art performance, outperforming the strongest comparator by 24.8% on average and by up to 73.9%. This study establishes, to our knowledge, the first foundation model for battery life prediction and points towards a shift from isolated, scenario-specific modelling to a reusable knowledge foundation for data-efficient specialization, with broader implications for sustainable-energy prediction problems constrained by scarce and heterogeneous data.