收藏切换
SOH Prediction of Electric Vehicle Lithium Battery Based on Integrated Model of Variation Modal Decomposition and Kernel-based Extreme Learning Machine
收藏切换
Chunling WU1, Jingjing LÜ1, Kang XIANGLI1, Jinhao MENG2, Xinrong HUANG1, Zhen ZHANG1
Journal of Power Supply | 2025, 23(6) : 288 - 299
Less
收藏切换
Journal of Power Supply | 2025, 23(6): 288-299
• Battery and Energy Storage •
SOH Prediction of Electric Vehicle Lithium Battery Based on Integrated Model of Variation Modal Decomposition and Kernel-based Extreme Learning Machine
Full
Chunling WU1, Jingjing LÜ1, Kang XIANGLI1, Jinhao MENG2, Xinrong HUANG1, Zhen ZHANG1
Affiliations
  • 1.School of Energy and Electrical Engineering, Chang'an University, Xi'an 710061, China
  • 2.School of Electrical Engineering, Xi'an Jiaotong University, Xi'an 710049, China
Published: 2025-10-30 doi: 10.13234/j.issn.2095-2805.2025.6.288
Outline
收藏切换

In the traditional prediction of lithium batteries for electric vehicles, the state-of-health (SOH) prediction is usually regarded as a whole, and the result of single SOH prediction is obtained accordingly. However, in the actual operation of a car, the single prediction of SOH has a large error, and its prediction effect is not satisfying. To improve the accuracy of battery SOH prediction for electric vehicles, a novel prediction method based on variational modal decomposition (VMD) and sparrow search algorithm (SSA) optimization of kernel-based extreme learning machine (KELM) integrated prediction model, i.e., VMD-SSA-KELM, is proposed. First, the battery SOH sequence is decomposed by VMD to reduce the influence of SOH fluctuations. Meanwhile, the Person correlation method is used to reduce the influence of noise and improve the accuracy of prediction. The KELM is introduced, which improves the accuracy of prediction while retaining the advantages of extreme learning machine. The proposed model is validated based on the operation data of four electric vehicles, and experimental results show that compared with the VMD-DBO-KELM, VMD-POA-KELM, VMD-KELM and VMD-ELM models, the proposed model has a prediction trend which is the same as that of the original data, while the results of other models fluctuate a lot. The root mean square error of results predicted by the novel model is less than 0.2%, the prediction accuracy becomes higher, the prediction efficiency is faster and the time used is shorter, indicating that the proposed method has higher accuracy and better robustness.

Lithium battery  /  variational modal decomposition (VMD)  /  kernel-based extreme learning machine (KELM)  /  sparrow search algorithm (SSA)
巫春玲, 吕晶晶, 相里康, 孟锦豪, 黄鑫蓉, 张震. 基于变分模态分解和核极限学习机集成模型的电动汽车锂电池健康状态预测. 电源学报, 2025 , 23 (6) : 288 -299 . DOI: 10.13234/j.issn.2095-2805.2025.6.288
Chunling WU, Jingjing LÜ, Kang XIANGLI, Jinhao MENG, Xinrong HUANG, Zhen ZHANG. SOH Prediction of Electric Vehicle Lithium Battery Based on Integrated Model of Variation Modal Decomposition and Kernel-based Extreme Learning Machine[J]. Journal of Power Supply, 2025 , 23 (6) : 288 -299 . DOI: 10.13234/j.issn.2095-2805.2025.6.288
  • National Key R&D Program(2021YFB2601300)
  • Shaanxi Province Key R&D Program(2022GY-193)
  • China Postdoctoral Science Foundation(2020M683414)
  • Shaanxi Province Natural Science Basic Research Program(2021JQ-251)
  • Shaanxi Province Science and Technology Major Special Project(2020zdzx06-01-01)
Year 2025 volume 23 Issue 6
54
8
Cite this Article
BibTeX
Article Info
doi: 10.13234/j.issn.2095-2805.2025.6.288
  • Receive Date:2023-04-18
  • Online Date:2026-10-26
  • Published:2025-10-30
Article Data
Affiliations
History
  • Received:2023-04-18
  • Revised:2023-08-02
  • Accepted:2023-08-16
Funding
National Key R&D Program(2021YFB2601300)
Shaanxi Province Key R&D Program(2022GY-193)
China Postdoctoral Science Foundation(2020M683414)
Shaanxi Province Natural Science Basic Research Program(2021JQ-251)
Shaanxi Province Science and Technology Major Special Project(2020zdzx06-01-01)
Affiliations
    1.School of Energy and Electrical Engineering, Chang'an University, Xi'an 710061, China
    2.School of Electrical Engineering, Xi'an Jiaotong University, Xi'an 710049, China
References
Share
https://castjournals.cast.org.cn/joweb/dyxb/EN/10.13234/j.issn.2095-2805.2025.6.288
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