Fault diagnosis of lithium-ion batteries is critical for system safety. While existing deep learning methods exhibit superior detection accuracy, their "black-box" nature hinders interpretability. Furthermore, restricted by binary classification paradigms, they struggle to provide root cause analysis and maintenance recommendations. To address these limitations, this paper proposes BatteryAgent, a hierarchical framework that integrates physical knowledge features with the reasoning capabilities of Large Language Models (LLMs). The framework comprises three core modules: (1) A Physical Perception Layer that utilizes 10 mechanism-based features derived from electrochemical principles, balancing dimensionality reduction with physical fidelity; (2) A Detection and Attribution Layer that employs Gradient Boosting Decision Trees and SHAP to quantify feature contributions; and (3) A Reasoning and Diagnosis Layer that leverages an LLM as the agent core. This layer constructs a "numerical-semantic" bridge, combining SHAP attributions with a mechanism knowledge base to generate comprehensive reports containing fault types, root cause analysis, and maintenance suggestions. Experimental results demonstrate that BatteryAgent effectively corrects misclassifications on hard boundary samples, achieving an AUROC of 0.986, which significantly outperforms current state-of-the-art methods. Moreover, the framework extends traditional binary detection to multi-type interpretable diagnosis, offering a new paradigm shift from "passive detection" to "intelligent diagnosis" for battery safety management.

翻译：锂离子电池故障诊断对系统安全至关重要。现有深度学习方法虽展现出优异的检测精度，但其"黑箱"特性阻碍了可解释性。此外，受限于二元分类范式，这些方法难以提供根本原因分析与维护建议。为突破这些局限，本文提出BatteryAgent——一个将物理知识特征与大型语言模型（LLM）推理能力相协同的分层框架。该框架包含三个核心模块：（1）物理感知层：利用基于电化学原理推导的10个机理特征，在降维与物理保真度间取得平衡；（2）检测归因层：采用梯度提升决策树与SHAP量化特征贡献度；（3）推理诊断层：以LLM作为智能体核心，构建"数值-语义"桥梁，结合SHAP归因结果与机理知识库，生成包含故障类型、根本原因分析和维护建议的完整诊断报告。实验结果表明，BatteryAgent能有效修正硬边界样本的误分类，AUROC达到0.986，显著优于当前最先进方法。此外，该框架将传统二元检测拓展为多类型可解释诊断，为电池安全管理提供了从"被动检测"到"智能诊断"的范式转换新路径。