基于大数据与机器学习的储能动力电池远程故障诊断研究

谭振国; 邓睿; 曾佳佳; 余聪

doi:10.19799/j.cnki.2095-4239.2026.0767

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基于大数据与机器学习的储能动力电池远程故障诊断研究

储能测试与评价 | 更新时间：2026-09-18

- 基于大数据与机器学习的储能动力电池远程故障诊断研究
- Research on remote fault diagnosis of energy storage power batteries based on big data and machine learning
- 储能科学与技术 2026年15卷第9期页码：3762-3764
- 作者机构：
  
  五凌电力有限公司新能源分公司，湖南长沙 410000
- 作者简介：
  
  谭振国（1978—），男，本科，高级工程师，研究方向为新能源生产管理，E-mail：tanzhenguo5050@163.com。
- 基金信息：
- DOI：10.19799/j.cnki.2095-4239.2026.0767
  中图分类号： TM 912
- 收稿：2026-09-04，
  
  修回：2026-09-14，
  
  纸质出版：2026-09-28
- 稿件说明：
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谭振国, 邓睿, 曾佳佳, 等. 基于大数据与机器学习的储能动力电池远程故障诊断研究[J]. 储能科学与技术, 2026, 15(9): 3762-3764.

TAN Zhenguo, DENG Rui, ZENG Jiajia, et al. Research on remote fault diagnosis of energy storage power batteries based on big data and machine learning[J]. Energy Storage Science and Technology, 2026, 15(9): 3762-3764.
谭振国, 邓睿, 曾佳佳, 等. 基于大数据与机器学习的储能动力电池远程故障诊断研究[J]. 储能科学与技术, 2026, 15(9): 3762-3764. DOI： 10.19799/j.cnki.2095-4239.2026.0767.

TAN Zhenguo, DENG Rui, ZENG Jiajia, et al. Research on remote fault diagnosis of energy storage power batteries based on big data and machine learning[J]. Energy Storage Science and Technology, 2026, 15(9): 3762-3764. DOI： 10.19799/j.cnki.2095-4239.2026.0767.

摘要

储能动力电池是新型电力系统储能环节、新能源交通领域的核心装备，其运行的安全稳定性，直接关乎电网储能系统的供电可靠性与终端设备的使用安全。作为储能动力电池全生命周期安全管控的核心手段，远程故障诊断的技术升级尤为关键。本文围绕大数据与机器学习技术在储能动力电池远程故障诊断领域的应用展开系统研究，搭建了全生命周期数据管控体系与多算法融合的故障诊断模型，可为相关系统的研发与工程化应用提供理论参考与技术支撑。

Abstract

Energy storage power batteries serve as the core equipment for the energy-storage segment of the new-type power system and the new-energy transportation sector. Their operational safety and stability are directly related to the power supply reliability of grid-scale energy storage systems and the operational safety of end-use equipment. As a core measure for the full-life-cycle safety management and control of energy storage power batteries

the technical upgrading of remote fault diagnosis is particularly critical. This paper systematically investigates the application of big data and machine learning technologies in the field of remote fault diagnosis for energy storage power batteries. A full-life-cycle data management-control system and a multi-algorithm-integrated fault diagnosis model are constructed

which can provide theoretical reference and technical support for the research-and-development and engineering application of relevant systems.

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references

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