Home Journals Articles Updates About
CN
image
收藏切换
Adaptive Particle Swarm Optimization-Support Vector Machine for Self-Discharge Diagnosis of Lithium-Ion Batteries
收藏切换
PDF
Chenghao LIU1, 2, Yuhao ZHANG2, Duanqian CHENG2, Fei YANG2, Yan FU2
Chinese Journal of Automotive Engineering | 2025, 15(2) : 147 - 154
Less
收藏切换
Chinese Journal of Automotive Engineering | 2025, 15(2): 147-154
• Green and Low-Carbon Technologies Section •
Adaptive Particle Swarm Optimization-Support Vector Machine for Self-Discharge Diagnosis of Lithium-Ion Batteries
Full
Chenghao LIU1, 2, Yuhao ZHANG2, Duanqian CHENG2, Fei YANG2, Yan FU2
Affiliations
  • 1 School of Big Data and Software Engineering，Chongqing University Chongqing 400044，China
  • 2 China Automotive Engineering Research Institute Co.，Ltd.，Chongqing 401122，China
Published: 2025-03-20 doi: 10.3969/j.issn.2095‒1469.2025.02.03
Outline
收藏切换

Lithium-ion power batteries are currently the most widely used energy storage devices in electric vehicles. Rapid and accurate battery fault diagnosis is crucial for ensuring safe vehicle operation. This paper proposes a method for diagnosing self-discharge faults in power batteries based on adaptive voltage thresholds for individual cells and Particle Swarm Optimization-Support Vector Machine (PSO-SVM). This study focuses on the voltage signals of power batteries, and combines the boxplot method with expert review to label self-discharge fault samples. A sliding window method is used to extract 16 features from both the time and frequency domains. To further reduce the dimensionality of voltage features, principal component analysis is applied, selecting the top five principal components with a 95% cumulative variance contribution as inputs for the PSO-SVM model. This method aims to improve the accuracy of self-discharge fault detection in batteries. The results show that the proposed method achieves high detection accuracy, strong reliability, and promising potential for practical applications in electric vehicles. Additionally, it provides theoretical support for enhancing the safety performance of electric vehicles.

power battery  /  voltage signal  /  self-discharge  /  support vector machine  /  principal component analysis
Chenghao LIU, Yuhao ZHANG, Duanqian CHENG, Fei YANG, Yan FU. Adaptive Particle Swarm Optimization-Support Vector Machine for Self-Discharge Diagnosis of Lithium-Ion Batteries[J]. Chinese Journal of Automotive Engineering, 2025 , 15 (2) : 147 -154 . DOI: 10.3969/j.issn.2095‒1469.2025.02.03
Appendix
Less
Year 2025 volume 15 Issue 2
PDF
353
133
Cite this Article
BibTeX
Article Info
doi: 10.3969/j.issn.2095‒1469.2025.02.03
  • Receive Date：2024-03-11
  • Online Date：2025-07-20
  • Published：2025-03-20
Article Data
Affiliations
History
  • Received：2024-03-11
  • Revised：2024-04-19
Funding
Affiliations
    1 School of Big Data and Software Engineering，Chongqing University Chongqing 400044，China
    2 China Automotive Engineering Research Institute Co.，Ltd.，Chongqing 401122，China
References
Share
https://castjournals.cast.org.cn/joweb/qcgcxb/EN/10.3969/j.issn.2095‒1469.2025.02.03
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT
Links
Articles
Latest Articles
Most Read
Collections
Updates
Events
News
Multimedia
About
About Us
Contact
No. 86 Xueyuan South Road, Haidian District, Beijing
100081
010-62199257
qkjq@cast.org.cn

Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House