西南交通大学学报2025，Vol.60Issue(3)：770-780,11.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
Lithium-Ion Battery Failure Identification Based on Segmented Penalty Parameter Support Vector Machine Algorithm
摘要
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 harmonic mean for precision and recall(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.关键词
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Key words
lithium-ion battery/failure identification/support vector machine/sample imbalance/segmented penalty parameter support vector machine分类
交通运输引用本文复制引用
金辉,胡寅逍,葛红娟,刘薇薇,李炳浩,李文臣,桑益芹..基于分段惩罚参数SVM算法的锂电池失效识别[J].西南交通大学学报,2025,60(3):770-780,11.基金项目
国家基金委民航联合基金项目(U2233205,U2133203) （U2233205,U2133203）
