用于电池 prognostics 与健康管理的大模型：综述与未来路线图
Large Models for Battery Prognostics and Health Management: A Review and Future Roadmap
- School of Physics and Astronomy, The University of Edinburgh（爱丁堡大学物理与天文学院）
- City University of Hong Kong（香港城市大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本综述首次全面调研大模型在电池 prognostics 与健康管理（BPHM）中的应用，阐明其基础技术、解决的领域挑战，提出未来研究路线图，为开发下一代自主电池管理系统提供见解。
AI中文摘要：
电池 prognostics 与健康管理（BPHM）对于确保电动汽车、电网储能和消费电子领域电池的安全、可靠且具成本效益的运行至关重要。传统 BPHM 方法，包括基于物理的模型和面向特定任务的深度学习方法，面临计算效率与参数化、跨域泛化、对大量标注的全生命周期运行数据的依赖以及模型可解释性等方面的挑战。近期基于 Transformer 架构和自监督预训练构建的大模型（LMs），为克服这些长期瓶颈提供了变革性的新范式。本综述是首次针对大模型在 BPHM 中应用的全面调研，系统考察了这些模型如何解决该领域的挑战。我们首先阐明支撑大模型的基础技术，包括 Transformer 架构、自监督学习、大规模多模态数据集以及 PEFT 技术。随后，我们从四个关键维度对近期进展进行分类：缓解数据稀缺、增强泛化性与鲁棒性、整合领域知识以提升可解释性、实现系统级自动化。尽管取得了令人鼓舞的结果，但在数据可获取性、智能性验证、可信度以及部署可行性方面仍存在重大挑战。为指导未来研究，我们提出了一条路线图，聚焦于构建协作式数据生态系统、验证工业应用的智能性、通过物理信息设计增强可信度以及实现高效的设备端部署。本综述建立了一种系统方法，以理解和推进大模型驱动的 BPHM，为研究人员和从业者提供开发下一代电池管理系统的必要见解，该系统可在电池全生命周期内实现安全、可靠且自主的运行。
英文摘要：
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.
