重庆理工大学学报2026，Vol.40Issue(3)：35-44,10.DOI:10.3969/j.issn.1674-8425(z).2026.02.005
基于KPCA与NRBO-Transformer的锂电池健康状态评估方法
Lithium battery health state assessment method based on KPCA and NRBO-Transformer
摘要
Abstract
The state of health(SOH)shows the aging state of lithium batteries.To accurately assess SOH,this paper first extracts six features in the current,voltage,and IC curves of the charging stage.To improve the quality of the input features,kernel principal component analysis(KPCA)combined with Spearman correlation analysis is employed to eliminate the redundancy of the multidimensional features and obtain the key information of the input features.Then,to reduce the model complexity,the fully connected layer is employed instead of the transformer decoder.The Newton-Raphson-based optimization algorithm(NRBO)is introduced to optimize the hyper-parameters of the model and improve the prediction accuracy.Finally,the effectiveness of the method is verified by using different training ratio divisions of the public dataset.Results show the proposed method excels in both accuracy and computational time compared with the gray wolf optimization algorithm(GWO)and the whale optimization algorithm(WOA).关键词
核主成分分析/牛顿-拉夫逊/Transformer模型/锂电池/健康状态Key words
kernel principal component analysis/Newton-Raphson/transformer model/lithium battery/state of health分类
信息技术与安全科学引用本文复制引用
刘富强,刘为国,朱洪波,胡凯,马旭东..基于KPCA与NRBO-Transformer的锂电池健康状态评估方法[J].重庆理工大学学报,2026,40(3):35-44,10.基金项目
国家自然科学基金项目(62003001) （62003001）
