Lithium-Ion Battery Failure Identification Based on Segmented Penalty Parameter Support Vector Machine Algorithm

JIN Hui; HU Yinxiao; GE Hongjuan; LIU Weiwei; LI Binghao; LI Wenchen; SANG Yiqin

doi:10.3969/j.issn.0258-2724.20230287

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Journal of Southwest Jiaotong University > 2025 > Accepted Manuscript

JIN Hui, HU Yinxiao, GE Hongjuan, LIU Weiwei, LI Binghao, LI Wenchen, SANG Yiqin. Lithium-Ion Battery Failure Identification Based on Segmented Penalty Parameter Support Vector Machine Algorithm[J]. Journal of Southwest Jiaotong University. doi: 10.3969/j.issn.0258-2724.20230287

Citation:

JIN Hui, HU Yinxiao, GE Hongjuan, LIU Weiwei, LI Binghao, LI Wenchen, SANG Yiqin. Lithium-Ion Battery Failure Identification Based on Segmented Penalty Parameter Support Vector Machine Algorithm[J]. Journal of Southwest Jiaotong University. doi: 10.3969/j.issn.0258-2724.20230287

Citation:

JIN Hui, HU Yinxiao, GE Hongjuan, LIU Weiwei, LI Binghao, LI Wenchen, SANG Yiqin. Lithium-Ion Battery Failure Identification Based on Segmented Penalty Parameter Support Vector Machine Algorithm[J]. Journal of Southwest Jiaotong University. doi: 10.3969/j.issn.0258-2724.20230287

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Lithium-Ion Battery Failure Identification Based on Segmented Penalty Parameter Support Vector Machine Algorithm

doi: 10.3969/j.issn.0258-2724.20230287

1.
College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
2.
China Academy of Civil Aviation Science and Technology, Beijing 100028, China

Received Date: 21 Jun 2023
Rev Recd Date: 25 Oct 2023

Available Online: 21 Feb 2025

Abstract

Abstract

In the application scenarios of unbalanced samples such as airborne lithium-ion battery failure identification, the support vector machine (SVM) algorithm has the problem of hyperplane offset separation. To address this issue, the segmented penalty parameter support vector machine (SPP-SVM) algorithm was proposed. The SPP-SVM divided all samples into different segments during the training process and automatically adjusted the penalty parameters of each sample based on the identification errors within each segment, thereby achieving hyperplane offset suppression. The features were extracted and screened based on capacity increment analysis and grey correlation analysis methods, and then, the lithium-ion battery failure identification model was established based on the SPP-SVM algorithm. By utilizing the NASA lithium-ion battery dataset and the University of California Irvine (UCI) datasets as experimental subjects, comparative experiments were conducted. The results show that the SPP-SVM algorithm has better identification performance than SVM combined with optimization algorithms. On the lithium-ion battery dataset with a large degree of imbalance, the F₁ score is improved by 11.7%. The SPP-SVM algorithm reduces the training time on the lithium-ion battery dataset and UCI dataset, offering a tenfold improvement. These results demonstrate that the SPP-SVM algorithm can effectively suppress hyperplane separation offset and improve identification performance in cases of sample imbalance.
- lithium-ion battery,
- failure identification,
- support vector machine,
- sample imbalance,
- segmented penalty parameter support vector machine

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References(29)

References

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