{"slug": "large-models-for-battery-prognostics-and-health-management-a-review-and-future", "title": "Large Models for Battery Prognostics and Health Management: A Review and Future Roadmap", "summary": "A review from arXiv (2608.26111v1) provides the first comprehensive survey of Large Models (LMs) for Battery Prognostics and Health Management (BPHM), finding that Transformer-based LMs can overcome bottlenecks in computational efficiency, cross-domain generalization, data labeling, and interpretability. The authors propose a roadmap for collaborative data ecosystems, industrial validation, physics-informed trustworthiness, and on-device deployment to advance next-generation battery management systems.", "body_md": "arXiv:2608.26111v1 Announce Type: new\nAbstract: Battery Prognostics and Health Management (BPHM) is critical for ensuring the safe, reliable, and cost-effective operation of batteries across electric vehicles, grid storage, and consumer electronics. Conventional BPHM approaches, including physics-based models and task-centric deep learning methods, face challenges in computational efficiency and parameterization, cross-domain generalization, dependence on extensive labeled run-to-failure data, and model interpretability. Recent Large Models (LMs), built upon Transformer architectures and self-supervised pre-training, offer a transformative new paradigm to overcome these long-standing bottlenecks. This review provides the first comprehensive survey of LM applications in BPHM, systematically examining how these models address challenges in the field. We begin by elucidating the foundational technologies enabling LMs, including Transformer architectures, self-supervised learning, large-scale multimodal datasets, and PEFT techniques. We then categorize recent progress along four critical dimensions: mitigating data scarcity, enhancing generalization and robustness, integrating domain knowledge for interpretability, and enabling system-level automation. Despite promising results, significant challenges remain across data accessibility, intelligence validation, trustworthiness, and deployment feasibility. To guide future research, we propose a roadmap focused on building collaborative data ecosystems, validating intelligence for industrial applications, enhancing trustworthiness with physics-informed designs, and enabling efficient on-device deployment. This review establishes a systematic approach to understand and advance LM-driven BPHM, providing researchers and practitioners with essential insights for developing next-generation battery management systems capable of safe, reliable, and autonomous operation throughout battery lifecycles.", "url": "https://wpnews.pro/news/large-models-for-battery-prognostics-and-health-management-a-review-and-future", "canonical_source": "https://arxiv.org/abs/2608.26111", "published_at": "2026-08-28 04:00:00+00:00", "updated_at": "2026-08-28 04:18:34.510598+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "large-language-models", "ai-research"], "entities": ["arXiv"], "alternates": {"html": "https://wpnews.pro/news/large-models-for-battery-prognostics-and-health-management-a-review-and-future", "markdown": "https://wpnews.pro/news/large-models-for-battery-prognostics-and-health-management-a-review-and-future.md", "text": "https://wpnews.pro/news/large-models-for-battery-prognostics-and-health-management-a-review-and-future.txt", "jsonld": "https://wpnews.pro/news/large-models-for-battery-prognostics-and-health-management-a-review-and-future.jsonld"}}