Home Journals Articles Updates About
CN
image
收藏切换
Battery SOH Prediction Based on Support Vector Regression Optimized by Genetic Algorithm
收藏切换
PDF
Shan He1, Xiongbo Hao2, 3, Yuming Zhao1, Ying Jiang2, 3, Haowei Li3
Automobile Technology | 2024, (5) : 31 - 36
Less
收藏切换
Automobile Technology | 2024, (5): 31-36
Battery SOH Prediction Based on Support Vector Regression Optimized by Genetic Algorithm
Full
Shan He1, Xiongbo Hao2, 3, Yuming Zhao1, Ying Jiang2, 3, Haowei Li3
Affiliations
  • 1 Shenzhen Power Supply Bureau Co.,Ltd., Shenzhen 518000
  • 2 Automotive Data of China (Tianjin) Co., Ltd., Tianjin 300000
  • 3 China Academy of Industrial Internet, Beijing 100000
Published: 2024-05-24 doi: 10.19620/j.cnki.1000-3703.20230606
Outline
收藏切换

The current available capacity of the battery is difficult to obtain, and the health status of the battery is difficult to estimate accurately during the operation of the vehicle. Therefore, this paper proposed to use the parking and charging segment data of the vehicle to correct the battery capacity obtained by ampere-hour integration method through box diagram and Kalman filter algorithm. The support vector regression model was constructed for battery degradation prediction. The effective model input parameters were determined by Pearson correlation analysis. The model parameters were optimized by genetic algorithm. Results show that the fitting accuracy of the optimized model reaches 88%, which is 12% higher than that before optimization, can accurately predict the SOH of vehicle battery.

Vehicle data  /  Power battery  /  Capacity degradation  /  Kalman filter  /  Genetic algorithm  /  Support vector regression
Shan He, Xiongbo Hao, Yuming Zhao, Ying Jiang, Haowei Li. Battery SOH Prediction Based on Support Vector Regression Optimized by Genetic Algorithm[J]. Automobile Technology, 2024 , (5) : 31 -36 . DOI: 10.19620/j.cnki.1000-3703.20230606
Appendix
Less
Year 2024 volume Issue 5
PDF
210
82
Cite this Article
BibTeX
Article Info
doi: 10.19620/j.cnki.1000-3703.20230606
  • Online Date：2025-12-23
  • Published：2024-05-24
Article Data
Affiliations
History
Affiliations
    1 Shenzhen Power Supply Bureau Co.,Ltd., Shenzhen 518000
    2 Automotive Data of China (Tianjin) Co., Ltd., Tianjin 300000
    3 China Academy of Industrial Internet, Beijing 100000
References
Share
https://castjournals.cast.org.cn/joweb/qcjs/EN/10.19620/j.cnki.1000-3703.20230606
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