基于EIS的燃料电池故障在线诊断研究进展

王宁; 杨萧潇; 孟海军; 郭建伟; 谢峰; 叶东浩; 朱荣杰; 邵志刚; 丁飞

doi:10.19799/j.cnki.2095-4239.2026.0333

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基于EIS的燃料电池故障在线诊断研究进展

储能测试与评价 | 更新时间：2026-09-18

- 基于EIS的燃料电池故障在线诊断研究进展
- Review of on-line fuel cell fault diagnosis based on EIS
- 储能科学与技术 2026年15卷第9期页码：3803-3823
- 作者机构：
  
  1.河北工业大学电气工程学院，智能配用电装备与系统全国重点实验室，天津 300130
  2.军事科学院某所，北京 100141
  3.清华大学核能与新能源技术研究院，北京 100084
  4.中国科学院大连化学物理研究所，辽宁大连 116023
  5.武汉船用电力推进装置研究所，湖北武汉 430064
  6.航天氢能（上海）科技有限公司，上海 200245
- 作者简介：
  
  王宁（1990—），男，博士，副教授，主要研究方向为电池储能技术，包括基于电化学阻抗谱的燃料电池、锂离子电池状态监测与故障诊断研究，E-mail：ningwang@hebut.edu.cn；
  邵志刚，博士，研究员，研究方向为车用质子交换膜燃料电池工程技术开发，E-mail：zhgshao@dicp.ac.cn
  丁飞，博士，教授，主要研究方向为电化学储能技术，E-mail：hilldingfei@163.com。
- 基金信息：
  
  河北省高等学校科学技术研究项目(BJK2024123)
- DOI：10.19799/j.cnki.2095-4239.2026.0333
  中图分类号： TM 911.4
- 收稿：2026-04-17，
  
  修回：2026-06-14，
  
  纸质出版：2026-09-28
- 稿件说明：
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王宁, 杨萧潇, 孟海军, 等. 基于EIS的燃料电池故障在线诊断研究进展[J]. 储能科学与技术, 2026, 15(9): 3803-3823.

WANG Ning, YANG Xiaoxiao, MENG Haijun, et al. Review of on-line fuel cell fault diagnosis based on EIS[J]. Energy Storage Science and Technology, 2026, 15(9): 3803-3823.
王宁, 杨萧潇, 孟海军, 等. 基于EIS的燃料电池故障在线诊断研究进展[J]. 储能科学与技术, 2026, 15(9): 3803-3823. DOI： 10.19799/j.cnki.2095-4239.2026.0333.

WANG Ning, YANG Xiaoxiao, MENG Haijun, et al. Review of on-line fuel cell fault diagnosis based on EIS[J]. Energy Storage Science and Technology, 2026, 15(9): 3803-3823. DOI： 10.19799/j.cnki.2095-4239.2026.0333.

摘要

燃料电池（fuel cell，FC）作为清洁高效的电化学能量转换装置，在交通运输、分布式发电等领域具备广阔应用前景，但运行过程中材料劣化、工况波动易引发各类故障，影响其可靠性与耐久性。电化学阻抗谱（electrochemical impedance spectroscopy，EIS）凭借非损伤扰动信号、多频率表征电池内部过程的独特优势，可精准解析电池内部欧姆损耗、电荷转移、传质过程等状态变化，成为FC故障诊断的核心技术。针对在线实时诊断的工程化需求，本文从在线诊断的全流程视角，系统梳理了基于EIS的FC故障诊断技术研究进展。在测量系统优化方面，从硬件集成、激励信号设计和输出信号处理三个维度总结了现有EIS测量技术的优化路径；在特征提取方面，归纳了等效电路模型拟合、弛豫时间分布解析以及卷积神经网络端到端学习等方法的适用场景与性能特点；在在线诊断策略方面，将现有方法划分为基于模型、数据驱动及融合方法三大类，结合诊断精度、响应时间与适用故障类型进行了综合对比。在此基础上，本文构建了涵盖材料型故障与工况诱导型故障的EIS特征信号归纳框架，以奈奎斯特图的图形变化模式为统一描述载体，凝练了各类故障在阻抗复平面上的共性特征，为不同诊断方法提供了参照体系。最后，展望了在线诊断技术在故障退化轨迹、极端工况适应性等方面的应用前景，为突破基于EIS的FC故障诊断技术的在线应用提供科学指导。

Abstract

As clean and efficient electrochemical energy conversion devices

fuel cells (FCs) hold broad prospects in various fields

including transportation and distributed power generation. However

material degradation and operational fluctuations induce various faults

thereby compromising FC reliability and durability. Electrochemical impedance spectroscopy (EIS)

leveraging its unique advantages of noninvasive perturbation signals and multifrequency characterization of internal processes

accurately resolves state changes in ohmic losses

charge transfer

and mass transport within FCs

making it a core technology for fault diagnosis. To satisfy the engineering requirements for online real-time diagnosis

this study systematically reviews the research progress on EIS-based FC-fault-diagnosis technologies from a full-process perspective of online diagnosis. Regarding measurement system optimization

existing EIS techniques are summarized from three dimensions: hardware integration

excitation-signal design

and output-signal processing. For feature extraction

the applicable scenarios and performance characteristics of methods including equivalent-circuit model fitting

distribution of relaxation times analysis

and convolutional neural network-based end-to-end learning are reviewed. For online diagnostic strategies

existing approaches are categorized into model-based

data-driven

and hybrid methods

and are comprehensively compared in terms of their diagnostic accuracy

response time

and applicable fault types. On this basis

an EIS characteristic-signal-induction framework spanning both material-induced and operating-condition-induced faults is constructed. Using Nyquist plot morphological evolution as a unified descriptive carrier

common fault characteristics on the complex impedance plane are condensed into a baseline reference for different diagnostic methods. Finally

the practical research and application outcomes of various diagnostic strategies across different FC types are summarized alongside material-induced and operating-condition-induced fault classifications. The application prospects of online diagnostic technologies for fault-degradation-trajectory modeling and extreme-condition adaptability are discussed

offering scientific guidance for overcoming the barriers to online EIS-based FC-fault diagnosis.

关键词

Keywords

references

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