Review outlines how large AI models could transform battery health monitoring

A new arXiv preprint reviews how large models built on Transformer architectures and self-supervised learning could address persistent challenges in battery prognostics and health management (BPHM). The survey highlights limitations of conventional approaches—such as physics-based models and task-specific deep learning—including computational inefficiency, reliance on extensive labeled run-to-failure data, and poor cross-domain generalization.

The authors categorize recent progress into four areas where large models show promise: reducing data scarcity, improving generalization and robustness, integrating domain knowledge for interpretability, and enabling system-level automation. They emphasize that while early results are encouraging, significant hurdles remain in data accessibility, validation for industrial use, trustworthiness, and deployment feasibility.

To guide future work, the paper proposes a research roadmap focused on building collaborative data ecosystems, validating model intelligence for industrial applications, enhancing trustworthiness through physics-informed designs, and enabling efficient on-device deployment. The review positions large-model-driven BPHM as a pathway toward next-generation battery management systems capable of safe, reliable, and autonomous operation across battery lifecycles.