基于分段惩罚参数SVM算法的锂电池失效识别

金辉; 胡寅逍; 葛红娟; 刘薇薇; 李炳浩; 李文臣; 桑益芹

doi:10.3969/j.issn.0258-2724.20230287

基于分段惩罚参数SVM算法的锂电池失效识别

doi: 10.3969/j.issn.0258-2724.20230287

1.
南京航空航天大学民航学院，江苏南京 211106
2.
中国民航科学技术研究院，北京 100028

基金项目: 国家基金委民航联合基金项目（U2233205，U2133203）

详细信息

作者简介:
金辉（1998—），男，博士研究生，研究方向为机载设备安全性，E-mail：jin_hui@nuaa.edu.cn

通讯作者:
葛红娟（1966—），女，教授，博士，研究方向为航空电源系统设计、验证与安全性评估，E-mail：allenge@nuaa.edu.cn

中图分类号: U262.44
计量
- 文章访问数: 26
- HTML全文浏览量: 13
- PDF下载量: 3
- 被引次数: 0
出版历程
- 收稿日期: 2023-06-21
- 修回日期: 2023-10-25
- 网络出版日期: 2025-02-21

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

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

摘要

摘要:
针对机载锂电池失效识别等样本不平衡的应用场景，支持向量机（support vector machine，SVM）算法存在分离超平面偏移问题，提出分段惩罚参数支持向量机（segmented penalty parameters support vector machine，SPP-SVM）算法. SPP-SVM在训练过程中将样本分段，根据各段内样本的识别误差，自动调整其惩罚参数，从而抑制超平面偏移；基于容量增量分析和灰色关联分析等方法提取并筛选特征，进而基于SPP-SVM算法建立锂电池失效识别模型；以NASA锂电池数据集和加州大学欧文分校（University of California Irvine，UCI）数据集为对象，开展对比实验. 研究结果表明：与结合寻优算法的SVM相比，SPP-SVM算法识别性能更好，在不平衡程度较大的锂电池数据上，F1值提升了11.7%；在锂电池数据集和UCI数据集上的训练耗时降低了10倍以上；证明在样本不平衡情况下，使用SPP-SVM算法能够有效抑制分离超平面偏移，提升识别效果.
- 锂离子电池 /
- 失效识别 /
- 支持向量机 /
- 样本不平衡 /
- 分段惩罚参数支持向量机
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

HTML全文

图 1 锂电池数据集训练中分离超平面偏移

Figure 1. Hyperplane offset separation in lithium-ion battery dataset training

下载: 全尺寸图片幻灯片

图 2 SPP-SVM算法流程

Figure 2. SPP-SVM algorithm flow

下载: 全尺寸图片幻灯片

图 3 滤波后不同充电次数下的IC曲线

Figure 3. IC curve under different charging times after filtering

下载: 全尺寸图片幻灯片

图 4 训练中SVM与SPP-SVM各指标变化对比

Figure 4. Comparison of changes in each index between SVM and SPP-SVM during training

下载: 全尺寸图片幻灯片

图 5 不同分段程度下SPP-SVM训练过程指标变化

Figure 5. Index changes during SPP-SVM training process under different segmentation levels

下载: 全尺寸图片幻灯片

表 1 NASA实验所用锂电池参数

Table 1. Parameters of lithium-ion battery used in NASA experiments

参数类型	参数值
电池型号	18650
最大充电截止电压/V	4.20
最小放电截止电压/V	2.75
额定电压/V	3.6
出厂额定容量/A·h	2
充电温度范围/℃	0～45
放电温度范围/℃	−20～60

下载: 导出CSV

表 2 锂电池数据集上识别效果

Table 2. Identification results on lithium-ion battery dataset

算法	准确率	查准率	查全率	F1	误差率	耗时/s
SVM	0.936	0.886	0.799	0.841	0.064	0.103
GA-SVM	0.978	0.928	0.969	0.948	0.022	64.399
PSO-SVM	0.978	0.928	0.969	0.948	0.022	24.424
SPP-SVM	0.979	0.954	0.95	0.952	0.021	1.391

下载: 导出CSV

表 3 样本不平衡的锂电池数据集T1上识别效果

Table 3. Identification results on lithium-ion battery dataset T1 with sample imbalance

算法	准确率	查准率	查全率	F1	误差率	耗时/s
SVM	0.929	0.852	0.688	0.807	0.071	0.081
GA-SVM	0.973	0.956	0.878	0.915	0.027	55.131
PSO-SVM	0.966	0.898	0.898	0.898	0.034	21.994
SPP-SVM	0.979	0.943	0.931	0.936	0.021	1.325

下载: 导出CSV

表 4 样本不平衡的锂电池数据集T2上识别效果

Table 4. Identification results on lithium-ion battery dataset T2 with sample imbalance

算法	准确率	查准率	查全率	F1	误差率	耗时/s
SVM	0.939	0	0	0	0.061	0.041
GA-SVM	0.962	0.75	0.563	0.643	0.038	31.211
PSO-SVM	0.962	0.75	0.563	0.643	0.038	12.407
SPP-SVM	0.985	0.857	0.919	0.883	0.015	0.935

下载: 导出CSV

表 5 UCI数据集识别效果

Table 5. Identification results on UCI datasets

数据集	算法	准确率	查准率	查全率	F1值	误差率	耗时/s
Ionosphere b vs g （112 113）（112 113）	SVM	0.886	0.938	0.833	0.882	0.114	0.075
	GA-SVM	0.943	1.000	0.841	0.914	0.057	403.936
	PSO-SVM	0.949	1.000	0.857	0.923	0.051	91.848
	SPP-SVM	0.950	0.960	0.900	0.929	0.050	2.833
Abalone 11 vs 16 （244 34）（243 33）	SVM	0.893	0	0	0	0.107	0.062
	GA-SVM	0.878	0.500	0.529	0.514	0.122	342.219
	PSO-SVM	0.878	0.500	0.529	0.514	0.122	98.610
	SPP-SVM	0.900	0.624	0.614	0.619	0.100	5.227
Ecoli im vs pp （39 26）（38 26）	SVM	0.809	0.759	0.679	0.717	0.191	0.014
	GA-SVM	0.908	0.813	1.000	0.897	0.092	17.118
	PSO-SVM	0.923	0.889	0.923	0.906	0.077	4.143
	SPP-SVM	0.940	0.913	0.942	0.927	0.060	0.368
Monk2 1 vs 2 （104 64）（289 142）	SVM	0.671	0	0	0	0.338	0.005
	GA-SVM	0.805	0.649	0.887	0.750	0.195	155.723
	PSO-SVM	0.821	0.688	0.838	0.756	0.179	52.762
	SPP-SVM	0.828	0.695	0.852	0.766	0.172	0.227
Monk3 1 vs 2 （59 62）（227 204）	SVM	0.912	0.874	0.951	0.911	0.088	0.002
	GA-SVM	0.961	0.979	0.936	0.957	0.039	59.241
	PSO-SVM	0.959	1.000	0.919	0.958	0.041	29.379
	SPP-SVM	0.963	0.948	0.975	0.961	0.037	0.095

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