基于多熵融合的动力电池故障诊断与应用研究

doi:10.3901/JME.2024.12.301

机械工程学报 ›› 2024, Vol. 60 ›› Issue (12): 301-312.doi: 10.3901/JME.2024.12.301

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基于多熵融合的动力电池故障诊断与应用研究

洪吉超^1,2, 张昕阳¹, 徐晓明^1,2, 李仁政³, 金帅阳¹

1. 北京科技大学机械工程学院北京 100083;
2. 北京科技大学顺德研究生院佛山 528000;
3. 同济大学汽车学院上海 201804

收稿日期:2023-08-15 修回日期:2024-01-20 出版日期:2024-06-20 发布日期:2024-08-23
作者简介:洪吉超(通信作者),男,1989年出生,博士,副教授。主要研究方向为新能源汽车动力系统集成、大数据挖掘分析与智能安全控制关键技术等。E-mail:hongjichao@ustb.edu.cn;张昕阳,男,1999年出生,硕士研究生。主要研究方向为新能源汽车动力电池系统安全监测与故障诊断。E-mail:zhangxyustb@163.com;徐晓明,男,1982年出生,博士,教授。主要研究方向为新能源汽车动力电池系统(锂离子电池/燃料电池)与储能技术等。Email:xuxiaoming3777@163.com;李仁政,男,1994年出生,博士研究生。主要研究方向为新能源汽车动力电池系统安全状态控制。Email:1911070@tongji.edu.cn;金帅阳,男,2003年出生。主要研究方向为新能源汽车安全监测与故障诊断。E-mail:jinshuaiyangustb@163.com
基金资助:
国家自然科学基金(52107220)和博士后科学基金(2021M690353)资助项目。

Research on Fault Diagnosis and Application of Battery Systems Based on Multi-entropy Fusion

HONG Jichao^1,2, ZHANG Xinyang¹, XU Xiaoming^1,2, LI Renzheng³, JIN Shuaiyang¹

1. School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083;
2. Shunde Graduate School of University of Science and Technology Beijing, Foshan 528000;
3. College of Automotive Studies, Tongji University, Shanghai 201804

Received:2023-08-15 Revised:2024-01-20 Online:2024-06-20 Published:2024-08-23

摘要/Abstract

摘要： 高效、稳定、可靠的动力电池故障诊断对于保证新能源汽车的安全运行至关重要。首先基于所筛选熵的本征分析，将其分为时序性熵与多尺度熵，并选取五种典型熵开展动力电池故障诊断效果验证，结果表明时序熵计算速度快、计算量低，适合进行实时故障诊断，而多尺度熵对于波动性异常的诊断能力更加突出。然后分别选取两种熵进一步探索关键计算因子和故障诊断效果之间的耦合关系，结果表明修正香农熵诊断效果与计算窗口之间呈近似对数函数演变规律，修正多尺度熵诊断效果与尺度因子之间近似正态分布关系。最后，提出面向实际工程应用的多熵融合故障诊断策略，并从在线状态识别、实时故障诊断与全面故障排查等方面给出具体应用思路。以上研究结果可以显著提升动力电池故障诊断效率和诊断覆盖面，实现高效率的动力电池在线状态监控和故障全面排查，对后续开发高安全性动力电池管理系统和健康监管系统具有重要的理论指导意义和广阔应用前景。

关键词: 新能源汽车, 动力电池, 故障诊断, 本征分析, 多熵融合

Abstract: Efficient, stable, and reliable battery fault diagnosis is crucial to ensure the safe operation of new energy vehicles. Firstly, based on the eigenanalysis of the screened entropy, it is divided into temporal entropy and multiscale entropy, and five typical entropies are selected to carry out the validation of power battery fault diagnosis effect. The results show that temporal entropy is fast and low in computation, which is suitable for real-time fault diagnosis, while multiscale entropy is more prominent for the diagnosis ability of fluctuating abnormalities. The results show that temporal entropy is fast and low computational power, which is suitable for real-time fault diagnosis, while multi-scale entropy is more prominent for the diagnosis of fluctuating abnormalities. Then the two entropies are selected separately to further explore the intrinsic relationship between key computational factors and fault diagnosis effect. The results show that the evolution law of logarithmic function is shown between the diagnosis effect of modified Shannon entropy and computational window, and the relationship between the diagnosis effect of modified multiscale entropy and scale factor is approximately normal distribution. Finally, a multi-entropy fusion fault diagnosis strategy for practical engineering applications is proposed for the comprehensive diagnosis of two fault types, namely, size class and fluctuation class, and specific application ideas are given in three aspects, namely, online state identification, real-time fault diagnosis and comprehensive fault troubleshooting. The above research results can significantly improve the efficiency and diagnostic coverage of power battery fault diagnosis, realize high-efficiency power battery online condition monitoring and comprehensive fault detection, and have important theoretical guidance significance and broad application prospects for the subsequent development of high-safety power battery management system and health supervision system.

Key words: new energy vehicles, power battery, fault diagnosis, intrinsic analysis, multi-entropy fusion

中图分类号:

TM911

洪吉超, 张昕阳, 徐晓明, 李仁政, 金帅阳. 基于多熵融合的动力电池故障诊断与应用研究[J]. 机械工程学报, 2024, 60(12): 301-312.

HONG Jichao, ZHANG Xinyang, XU Xiaoming, LI Renzheng, JIN Shuaiyang. Research on Fault Diagnosis and Application of Battery Systems Based on Multi-entropy Fusion[J]. Journal of Mechanical Engineering, 2024, 60(12): 301-312.

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链接本文: http://www.cjmenet.com.cn/CN/10.3901/JME.2024.12.301

http://www.cjmenet.com.cn/CN/Y2024/V60/I12/301

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基于多熵融合的动力电池故障诊断与应用研究

Research on Fault Diagnosis and Application of Battery Systems Based on Multi-entropy Fusion

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