Large Models for Battery Prognostics and Health Management: A Review and Future Roadmap

面向电池预测与健康管理的大模型：综述与未来路线图

Abstract: 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.

摘要： 电池预测与健康管理（BPHM）对于确保电动汽车、电网储能和消费电子产品中电池的安全、可靠及经济高效运行至关重要。传统的 BPHM 方法（包括基于物理的模型和以任务为中心的深度学习方法）在计算效率与参数化、跨领域泛化能力、对大规模标注失效数据的依赖以及模型可解释性等方面面临挑战。

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.

基于 Transformer 架构和自监督预训练的近期大模型（LMs），为克服这些长期存在的瓶颈提供了一种变革性的新范式。本综述首次全面调研了大模型在 BPHM 中的应用，系统地探讨了这些模型如何解决该领域面临的挑战。

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.

我们首先阐述了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参数高效微调（PEFT）技术。随后，我们从四个关键维度对近期进展进行了分类：缓解数据稀缺性、增强泛化能力与鲁棒性、整合领域知识以提升可解释性，以及实现系统级自动化。

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.

尽管成果显著，但在数据可访问性、智能验证、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，我们提出了一份路线图，重点关注构建协作式数据生态系统、验证工业应用中的智能水平、通过物理信息驱动的设计增强可信度，以及实现高效的端侧部署。

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.

本综述建立了一套系统性方法来理解并推动大模型驱动的 BPHM，为研究人员和从业者提供了关键见解，助力开发能够在电池全生命周期内实现安全、可靠和自主运行的下一代电池管理系统。