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BatteryAgent: Synergizing Physics-Informed Interpretation with LLM Reasoning for Intelligent Battery Fault Diagnosis

Songqi Zhou, Ruixue Liu, Boman Su, Jiazhou Wang, Yixing Wang, Benben Jiang

TL;DR

BatteryAgent tackles the interpretability gap in lithium-ion battery fault diagnosis by integrating physics-informed feature engineering with SHAP-based attribution and LLM-driven reasoning. The framework consists of three layers—Physics Perception, Detection & Attribution, and Reasoning & Diagnosis—connected via a numeric-to-semantic bridge that grounds LLM outputs in electrochemical principles. Empirical results on a large EV battery dataset show an AUROC of 0.986 and substantial reductions in operational cost, while ablation studies confirm the importance of attribution and domain knowledge for accurate, multi-type fault diagnosis. This approach moves beyond binary detection to intelligent, root-cause analysis and maintenance recommendations, offering significant practical impact for battery safety management and maintenance planning.

Abstract

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.

BatteryAgent: Synergizing Physics-Informed Interpretation with LLM Reasoning for Intelligent Battery Fault Diagnosis

TL;DR

BatteryAgent tackles the interpretability gap in lithium-ion battery fault diagnosis by integrating physics-informed feature engineering with SHAP-based attribution and LLM-driven reasoning. The framework consists of three layers—Physics Perception, Detection & Attribution, and Reasoning & Diagnosis—connected via a numeric-to-semantic bridge that grounds LLM outputs in electrochemical principles. Empirical results on a large EV battery dataset show an AUROC of 0.986 and substantial reductions in operational cost, while ablation studies confirm the importance of attribution and domain knowledge for accurate, multi-type fault diagnosis. This approach moves beyond binary detection to intelligent, root-cause analysis and maintenance recommendations, offering significant practical impact for battery safety management and maintenance planning.

Abstract

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.
Paper Structure (25 sections, 6 equations, 3 figures, 3 tables)

This paper contains 25 sections, 6 equations, 3 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Proposed Methodology
  3. Framework Overview
  4. Physics Perception Layer
  5. Usage History Features
  6. Voltage Characteristics
  7. Thermal Dynamics
  8. Numeric-to-Semantic Bridge
  9. Detection & Attribution Layer
  10. Reasoning & Diagnosis Layer
  11. LLM Agent Architecture
  12. Probability Calibration via Token Likelihood
  13. Experimental Setup
  14. Dataset
  15. Experimental Design
  16. ...and 10 more sections

Figures (3)

  • Figure 1: The BatteryAgent framework architecture. The framework comprises three layers: (1) Physics Perception Layer for mechanism-driven feature extraction, (2) Detection & Attribution Layer using GBDT and SHAP for fault classification and feature contribution analysis, and (3) Reasoning & Diagnosis Layer where an LLM agent generates diagnostic reports with root cause analysis and maintenance recommendations via the Numeric-to-Semantic Bridge.
  • Figure 2: Ablation study results showing classification distributions. (a) Ground truth: Abnormal samples ($N=100$). (b) Ground truth: Healthy samples ($N=100$). The "warning" category represents cases where the model expresses uncertainty.
  • Figure 3: Fault severity profile of vehicle 405 over 50 charging segments. Solid line: mean rating; shaded region: standard deviation. Higher values indicate greater fault severity (scale: 0--5).